&EPA
UnitedStates EPA-600 /7-91-Q06
Environmental Protection
Agency November 1991
Research and
Development
DEVELOPMENT OF
SEASONAL AND ANNUAL
BIOGENIC EMISSIONS INVENTORIES
FOR THE U. S. AND CANADA
Prepared for
Office of Air Quality Planning and Standards
Prepared by
Air and Energy Engineering Research
Laboratory
Research Triangle Park NC 27711
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7. Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the INTERAGENCY ENERGY-ENVIRONMENT
RESEARCH AND DEVELOPMENT series. Reports in this series result from the
effort funded under the 17-agency Federal Energy/Environment Research and
Development Program. These studies relate to EPA's mission to protect the public
health and welfare from adverse effects of pollutants associated with energy sys-
tems. The goal of the Program is to assure the rapid development of domestic
energy supplies in an environmentally-compatible manner by providing the nec-
essary environmental data and control technology. Investigations include analy-
ses of the transport of energy-related pollutants and their health and ecological
effects; assessments of, and development of, control technologies for energy
systems; and integrated assessments of a wide range of energy-related environ-
mental issues.
EPA REVIEW NOTICE
This report has been reviewed by the participating Federal Agencies, and approved
for publication. Approval does not signify that the contents necessarily reflect
the views and policies of the Government, nor does mention of trade names or
commercial products constitute endorsement or recommendation for use.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/7-91-006
November 1991
DEVELOPMENT OF SEASONAL AND
ANNUAL BIOGEN1C EMISSIONS INVENTORIES
FOR THE U.S. AND CANADA
by
Lysa G. Modica
John R. McCutcheon
ALLIANCE TECHNOLOGIES CORPORATION
Boott Mills South
Foot of John Street
Lowell, MA 01852
EPA Contract 68-D9-0173
Work Assignment 1/113
EPA Project Officer: Christopher D. Geron
Air and Energy Engineering Research Laboratory
Research Triangle Park, NC 27711
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Washington, DC 20460
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ACKNOWLEDGEMENTS
This report describes the processing of a biogenic hydrocarbon emissions inventory, a project
funded by the U.S. Environmental Protection Agency's Air and Energy Engineering Research
Laboratory. The authors wish to express their gratitude to Tom Pierce (EPA/AREAL) and
David Turner (EPA/Corvallis) for their time and effort in reviewing the earlier version of this
report. Their comments and suggestions provided valuable guidance in producing the current
version of the inventory and report. The authors also would like to thank Tom Pierce for his
invaluable assistance in implementing the modified solar radiation algorithm used in this
project and David Mobley of the Emission Inventory Branch at EPA/OAQPS for his
invaluable guidance and insight.
11
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TABLE OF CONTENTS
Section Page
Acknowledgements »
Figures iv
Tables v
Metric Equivalents vii
1. INTRODUCTION 1
Background 1
Objectives 2
, Project Approach 2
Report Organization 5
2. DEVELOPMENT OF REPRESENTATIVE GRIDDED DIURNAL
METEOROLOGICAL PROFILES 6
Collection and Processing of Meteorological Data 6
Calculation of Representative Diurnal Profiles 11
Spatial Interpolation 15
Solar Radiation 19
3. CALCULATION OF BIOGENIC HYDROCARBON EMISSIONS 21
Calculation of Biomass 21
Calculation of Correction Factors 23
Calculation of Biogenic Hydrocarbon Emissions 24
Spatial and Temporal Aggregation of Emissions Data 24
4. NATURAL PARTICULATE AND BIOGENIC EMISSIONS DATA 26
Paniculate Matter 26
Biogenic Hydrocarbon and Grassland NOX Emissions 28
5. SUMMARY AND RECOMMENDATIONS 48
Summary 48
Recommendations 49
6. REFERENCES 50
Appendices
A Biogenic Emissions Data Summaries A-l
B Regional Emissions Processing for the RADM: Biogenic Sources B-l
C Grid Plots of Interpolated Meteorological Data for Select Months C-l
D Biogenic Emissions and Solar Radiation Source Code Listings D-l
iii
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LIST OF FIGURES
Number Page
1-1 Meteorological Stations Analyzed for the U.S. and Canada 4
2-1 Plot of Interpolation Test Results 18
4-1 Seasonal Distribution of Total Biogenic Hydrocarbon and Grassland NO,
for the United States 30
4-2 Seasonal Distribution of Total Biogenic Hydrocarbon and Grassland NOX
for Canada 31
4-3 Distribution of Biogenic Hydrocarbon Components for Each Season for
the United States 33
4-4 Distribution of Biogenic Hydrocarbon Components for Each Season for
Canada 34
4-5 Seasonal Gridded Biogenic Emissions of Isoprene for Summer 38
4-6 Seasonal Gridded Biogenic Emissions of Other Monoterpenes for Summer 39
4-7 Seasonal Gridded Biogenic Emissions of Alpha-Pinene for Summer 40
4-8 Seasonal Gridded Biogenic Emissions of Unknown Hydrocarbons for Summer ... 41
4-9 Seasonal Gridded Biogenic Emissions of Total Hydrocarbons for Summer 42
4-10 Annual Gridded Biogenic Emissions of Total Hydrocarbons 43
4-11 Annual Gridded Biogenic Emissions of Grassland NOX 45
C-l Monthly Average Temperature for January C-2
C-2 Monthly Average Temperature for April C-3
C-3 Monthly Average Temperature for July C-4
C-4 Monthly Average Temperature for October C-5
C-5 Monthly Average Attenuated Visible Solar Radiation for January C-6
C-6 Monthly Average Attenuated Visible Solar Radiation for April C-7
iv
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LIST OF FIGURES (CONTINUED)
Number Page
C-7 Monthly Average Attenuated Visible Solar Radiation for July C-8
C-8 Monthly Average Attenuated Visible Solar Radiation for October C-9
C-9 Monthly Average Sky Cover for January C-10
C-10 Monthly Average Sky Cover for April C-l 1
C-l 1 Monthly Average Sky Cover for July C-12
C-12 Monthly Average Sky Cover for October C-13
C-13 Monthly Average Wind Speed for January C-14
C-14 Monthly Average Wind Speed for April C-15
C-15 Monthly Average Wind Speed for July C-16
C-16 Monthly Average Wind Speed for October C-17
C-17 Monthly Average Relative Humidity for January C-18
C-1& Monthly Average Relative Humidity for April C-19
C-19 Monthly Average Relative Humidity for July C-20
C-20 Monthly Average Relative Humidity for October C-21
LIST OF TABLES
Number Page
2-1 Suspect Meteorological Data Values - U.S 8
2-2 Suspect Meteorological Data Values - Canada 11
2-3 Number of Meteorological Sites With Statistics Based on Less Than
One-Third of Potentially Available Data - U.S 13
2-4 Number of Meteorological Sites With Statistics Based on Less Than
One-Third of Potentially Available Data - Canada 13
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LIST OF TABLES (CONTINUED)
Number Page
4-1 Tape Totals for Combined U.S. and Canadian Natural Paniculate Sources 2^
4-2 Biogenic Hydrocarbon and Grassland NOX Emissions Summary 29
4-3 Seasonal Totals for Biogenic Emissions Inventories 36
4-4 Compound Specific Annual Totals for Biogenic Emissions Inventories 36
A-l 1985 NAPAP Modelers' Emission Inventory Version 2 (Revised) U.S. Natural
Source Particulate Emissions by State A-3
A-2 1985 NAPAP Modelers' Emission Inventory Version 2 (Revised) U.S. Natural
Source Particulate Emissions by EPA Region A-5
A-3 1985 NAPAP Modelers' Emission Inventory Version 2 (Revised) - U.S. Natural
Source Particulate Emissions by Source Category A-6
A-4 1985 NAPAP Modelers' Emission Inventory Version 2 Canadian Natural Source
Particulate Emissions by Province A-7
A-5 1985 NAPAP Modelers' Emission Inventory Version 2 - Canadian Natural Source
Particulate Emissions by Source Category A-8
A-6 Biogenic Emissions Estimates for the United States by State, Winter Season .... A-9
A-7 Biogenic Emissions Estimates for the United States by State, Spring Season ... A-10
A-8 Biogenic Emissions Estimates for the United States by State, Summer Season . . A-11
A-9 Biogenic Emissions Estimates for the United States by State, Autumn Season . . A-12
A-10 Biogenic Emissions Estimates for Canada, by Province, Winter Season A-13
A-11 Biogenic Emissions Estimates for Canada, by Province, Spring Season A-13
A-12 Biogenic Emissions Estimates for Canada, by Province, Summer Season A-14
A-13 Biogenic Emissions Estimates for Canada, by Province, Autumn Season A-14
VI
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Metric Equivalents
Users more familiar with metric units may us the following factors to convert the nonmetric
units presented in this document to that system:
Nonmetric Multiply by Yields Metric
°F 5/9 CF-32) °C
knot 0.514 m/s
mph 1.609 km/hr
short ton 907.18 kg
Vll
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SECTION 1
INTRODUCTION
The National Acid Precipitation Assessment Program (NAPAP) was established by Congress
in 1980 to expand the understanding of the processes that result in acid deposition phenomena
in and around the United States. One of the principal objectives of NAPAP was to develop a
complete and accurate inventory of natural and anthropogenic emissions of acid deposition
precursors. The 1985 NAPAP Emissions Inventory (Version 2) was delivered in February
1990. This inventory included anthropogenic emission data for SO2, NOX, NO, NO2, VOC,
_jrHCs_CO, TSP, NH?, SO4, HC1, HF, 32Jhyjrocarbon reactivity classes and 15 classes of
paniculate based on reactivity and size class. Emissions data were also developed for 12
classes of natural paniculate data based on reactivity and size classes.
The development of the emissions algorithms and supporting data for the calculation of
biogenic emissions was not sufficiently advanced to allow the inclusion of natural
hydrocarbon and NOX emissions in the NAPAP Version 2 inventory. Biogenic emissions
algorithms, which depend on meteorological data inputs, were made available shortly after the
completion of the NAPAP inventory. These algorithms can be used to estimate emissions of
isoprene. alpha pinene, other monoterpenes, unknown hydrocarbons, NO, and NO2. For this
inventory, only grassland NO, emissions were considered. Other sources of biogenic NOX are
known to exist but have not been well quantified to date. A methodology to apply these
algorithms was developed by the EPA Atmospheric Research and Exposure Assessment
Laboratory (AREAL) for episodic (day specific) simulations using the Regional Acid
Deposition Model (RADM) for model evaluation and research purposes.
While the availability of the episodic emissions estimates were valuable for application to the
specific days selected for the RADM evaluation simulations, it was desirable to develop
representative seasonal and annual emissions estimates for other NAPAP and EPA analyses.
Earlier efforts, performed by researchers at the Washington State University1'2 were based at
the county level and relied on monthly average meteorological data (e.g., temperature and
wind speed). The emissions rates calculated by the emissions algorithms are highly
dependent on hourly temperature and solar radiation data. A comparison of the results of this
study with earlier efforts is presented with the emissions data. In general, biogenic^
hydrocarbon emissions estimated using data and methodologies presented in this report are
lower than those reported in earlier efforts on an annual and seasonal basis.
BACKGROUND
Historically, ozone control programs based on reductions of emissions from identified
anthropogenic sources of volatile organic compounds (VOCs) have had little success. As a
result, researchers have been searching for categories of VOC emissions which have not been
routinely considered in the evaluation of ozone control strategies. One potentially large
source of reactive VOCs in certain areas is thought to be emissions resulting from biogenic
processes in forest and crop biomass3'4. Although the details regarding the emission
1
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mechanisms and the controlling factors affecting biogenic sources are not well understood,
significant advances have been made in attempts to quantify these emissions source
strengths1'2.
Biogenic VOC emissions can affect the atmospheric chemistry of urban ozone plumes when
they are introduced to an urban area as a background flux3. In addition, these emissions can
react with small amounts of NOX left over from urban processes or with additional natural
sources of NOX. The principal known sources of VOC from natural processes are direct
emissions from the leaf surface of forest biomass and agricultural crops. Emissions of NOX
from natural sources are thought to arise from chemistry and biochemistry in soils and from
lightning. Natural sources of other air pollutants may also be important for other
environmental concerns. For example, emissions of natural paniculate can have effects on
visibility and the alkaline components of paniculate may interact in the atmospheric and cloud
chemistry of acid rain.
OBJECTIVES
Thejjurpose of the research desjgrib^d^njhis^eport is to develop representative monthly,
seasonal and annual emissions estimates for natural sources of VOTTah3"NOx. Since the
emissions algorithms available rely on meteorologicaTdatzTas input, diurnal profiles for each
month were developed for a three year period that would be representative of the
meteorological conditions around the year 1985. The representative diurnal meteorological
parameters were spatially interpolated to 1/6 degree latitude by 1/4 degree longitude grid cells
and were used to calculate biogenic emissions using gridded land cover data with the same
spatial resolution. The calculated emissions were then aggregated spatially to the county and
State levels, and temporally to monthly, seasonal, and annual levels. The resulting database
provides estimates of biogenic emissions that rely on spatially and temporally variable
conditions, but are represented at larger spatial and temporal scales for use in emissions
assessment evaluations comparing the magnitude of anthropogenic and natural sources.
Biogenic emissions data summaries are presented in Section 4 and Appendix A.
A secondary objective of the research was to process an updated version of the county level
natural paniculate data which was developed after the completion of the 1985 NAPAP
Emissions Inventory (Version 2). The updated natural paniculate data incorporated
improvements to the emissions calculation methodologies for dust resulting from unpaved
road travel in the United States and improvements in the State to county allocation
methodologies. The county level data were processed using the NAPAP inventory allocation
software known as the Flexible Regional Emissions Data System (FREDS)5 that was used in
the development of the Version 2 NAPAP inventory.6 A summary of these data is presented
in Appendix A.
PROJECT APPROACH
The methodology used to develop the representative hourly, monthly, seasonal and annual
diurnal profiles of biogenic hydrocarbon and soil NO, emissions is outlined below. Details of
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the methodologies and quality assurance activities are presented in subsequent sections of this
report.
Three years of hourly surface airways meteorological data from the National Climatic Data
Center, reported at over 300 measurement sites in the United States, were obtained and
quality checked. Data at over 130 measurement sites in Canada were obtained from the
Canadian Climate Centre of Environment Canada. These data were used to develop diurnal
profiles representative of each month of the year at each reporting site. The spatial
distribution of meteorological stations analyzed is presented in Figure 1-1. These data were
spatially interpolated to generate monthly average diurnal profiles for the entire study region
in a grid based system defined by grid cells of 1/4 degree longitude by 1/6 degree latitude.
Gridded land use cover data were available from the NAPAP program. Leaf biomass data
were available at the county level from the Oak Ridge National Laboratory Geoecology data
base. These data were disaggregated to the grid level using gridded land use/cover data.
Documentation of these data are provided in Appendix B.
Biogenic hydrocarbon emissions were calculated for the representative hour and day in each
grid cell for each month of the year using the Canopy Emissions Model developed for
NAPAP by researchers at the Washington State University.2 The Canopy Model considers the
leaf temperature and solar radiation gradient within the forest canopy. Since the emissions
from trees are highly dependent on both temperature and solar radiation this algorithm
provides more representative estimates of the emissions rates than does a simpler treatment
based on the assumption that all of the biomass is exposed to the same unattenuated solar
radiation intensity. Algorithms were provided by NOAA to calculate emissions of NOX from
undisturbed (uncultivated) grassland areas. Similar to the biogenic hydrocarbons, grassland
NO, emissions are also dependent are temperature. NOX emissions algorithms for other land
use types were not available for application to this study. Additional emissions of NOX from
soils in forests, from agricultural lands, for deserts and from wetlands have been observed in
measurement programs, however, the dependencies on meteorological and other factors were
not yet determined for use in this effort.
The resulting hourly gridded emissions calculations were aggregated to develop monthly mean
emissions magnitudes at the grid level. Allocation factors, based on the grid/county overlap,
were used to aggregate the gridded emissions to county and then State totals. Finally, the
monthly averages were aggregated to seasonal and annual totals. The seasonal emissions
were developed on the following basis:
Winter December, January, February
Spring March, April, May
Summer June, July, August
Autumn September, October, November
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Figure 1-1. Meteorological Stations Analyzed for the United States and Canada.
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REPORT ORGANIZATION
The primary objectives of this report are to document the development of a biogenic
hydrocarbon emissions inventory using representative monthly diurnal profiles of
meteorological data and the implementation of the Canopy Model software. A summary of
the calculated emissions at varying levels of spatial and temporal aggregation is also
presented.
The remainder of this document is comprised of the following sections:
Section 2: Development of Representative Gridded Diurnal Meteorological Profiles
Section 3: Calculation of Biogenic Hydrocarbon Emissions
Section 4: Natural Paniculate and Biogenic Emissions Data
Section 5: Summary and Recommendations.
The emissions calculation methodology and summary for grassland NOX emissions are also
provided in Sections 3 and 4.
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SECTION 2
DEVELOPMENT OF REPRESENTATIVE GRIDDED
DIURNAL METEOROLOGICAL PROFILES
Biogenic emissions flux algorithms are expressed as a function of leaf temperature and solar
radiation. In forested areas, the Canopy Model corrects for leaf temperature and solar
radiation based on the vertical structure of the forest vegetation and meteorological data.
Hourly meteorological data for specific parameters are required for input to the Canopy
Model to calculate emission rates of biogenic hydrocarbons and NOX. These include surface
temperature, incident solar radiation, cloud cover, relative humidity and wind speed. The
incident solar radiation is adjusted to account for attenuation of incoming solar radiation by
cloud cover. In order to develop seasonal and annual biogenic emission estimates,
representative gridded monthly diurnal profiles were developed using three years of hourly
surface meteorological data.
Two meteorological data bases were used to generate representative monthly diurnal profiles
for the United States and Canada. Hourly surface meteorological data for the United States
were obtained from the National Climatic Data Center (NCDC) Surface Airways Hourly Files
(TD-3280)7 for 1984, 1985, and 1986. The meteorological data for Canada were supplied by
Environment Canada in the NCDC TDF-1440 format8 for 1983, 1984, and 1985. Three
concurrent years of meteorological data for the United States and Canada were not available;
the 1986 data for Canada were not available and meteorological data for 1983 for the United
States were not complete.
Generation of gridded representative hourly diurnal profiles was accomplished in three
phases: (1) collect, process, analyze and quality assure the meteorological data; (2) develop
representative hourly diurnal profiles based on statistical analyses; and (3) interpolate the
profiles to fill in data for grid cells for which no meteorological data were present.
Additionally, solar radiation for each 1/4 degree longitude by 1/6 degree latitude grid cell was
calculated for the midpoint day for each month as a function of latitude, longitude, day of the
year, and hour of the day. Solar radiation is attenuated for cloud cover prior to input into the
Canopy Model. Throughout each phase, quality control checks were performed to assure the
completeness and validity of the data. Each of these phases and quality control checks is
discussed in more detail in the following pages.
COLLECTION AND PROCESSING OF METEOROLOGICAL DATA
During the first phase, three years of meteorological data were obtained for the United States
and Canada. The data were provided in Surface Airways Formats TD-3280 and TDF-1440,
respectively. In the Surface Airways File TD-3280,7 each logical record contains hourly data
values for one station for a specific meteorological parameter for one day. Preceding the
hourly meteorological data values, each logical record contains a control variable and
identification information. The control variable contains the record length for each logical
record. The identification information includes the record type (e.g., hourly), station
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identification, meteorological parameter and units, year, month, day, and the number of values
in the record.
The Surface Airways TDF-1440 File8 contains four physical records for each twenty-four hour
period. Each physical record contains six logical records which contain observations for a six
hour period. Each physical record contains an identification portion and is followed by six
logical 80 byte records with meteorological observations. These records always begin with
the hour (local standard time, LST).
U.S. Meteorological Data Processing and Quality Control
Twelve magnetic tapes were read and processed on the National Computer Center's (NCC)
VAX computer to obtain hourly values of temperature, cloud cover, relative humidity and
wind speed over a three-year period. The data required for this effort were extracted from the
original NCDC data tapes using a Fortran program provided by EPA's Atmospheric Research
Exposure and Assessment Laboratory (AREAL). The data were transferred to the NCC IBM
for further processing and quality control checks.
Quality control checks were performed on the three years of data to assess the completeness
of the data before any processing was initiated. As a preliminary check, the number of
records in each of the three years of data were tallied and compared. This indicated that for
1984, there was approximately 400,000 fewer records than for 1985 or 1986. A comparison
of the 1984 data with that on the tapes for 1985 and 1986 indicated that the missing stations
were located in the northwest and northern plains sections of the U.S. Further evaluation
indicated that the missing data were the result of a physical flaw on one of the 1984
meteorological data tapes. These problems were corrected and the missing data were
obtained. The geographical coverage of the meteorological recording sites provided on the
NCDC tapes is illustrated in Figure 1-1. The number of sites available for each year are 307,
299, and 302 for 1984, 1985, and 1986, respectively. It should be noted that Figure 1-1
contains "cloned" meteorological data sites in areas of sparse coverage. The procedures and
necessity of the "cloned" sites is discussed under Spatial Interpolation.
To further assess the completeness of the data, the number of records available per site per
year were determined in order to identify potential data gaps during the period of record.
Stations indicating less than 8760 hours per year (for 1984, less than 8784 hours per year)
were identified and output for further evaluation. This analysis indicated that for 1984, 16
stations reported observations for less than 8784 hours. In 1985, 19 stations had fewer than
8760 observations and 22 stations did not report a full year of data in 1986. It should be
noted that this preliminary analysis did not assess the frequency of missing data for hours
contained within each file. Determination of missing data will be discussed later.
Each of the sites reporting less than a full year of data were examined to determine if the
missing data were scattered randomly over individual hours throughout the year or were
missing in blocks of hours (e.g., over the large part of a month or over several months). This
was accomplished by summing over the number of hours for each day for each month. The
results of this analysis indicated that for most of the sites reporting less than a full year of
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observations, entire days and sometimes several months were missing from the file rather than
sporadic missing hours. The impact of the missing data on the representativeness of the
diurnal profiles was evaluated while calculating univariate statistics over the three year period.
This will be discussed further in Calculation of Representative Diurnal Profiles.
Once the completeness of the data was determined and potential data gaps identified, error
checks were performed on the values reported for temperature, relative humidity, wind speed
and total cloud cover for the three year period. The acceptable data ranges used as criteria
for each parameter and the suspect values noted as a result of this analysis are presented in
Table 2-1. Dew point values were also checked to help identify any potentially suspect
relative humidity values. The data in Table 2-1 indicate that for the 7,351,532 observations
evaluated, very few contained suspect values. The 42 occurrences of 999 for wind speed are
most likely miscoded missing values (the missing value code for wind speed is -999).
Records with suspect values were checked to assure they were not the result of a short lived
weather anomaly (e.g.. the high wind speed values were checked to see if they corresponded
to changes in pressure, wind direction, and temperature as would be common with the
passage of a gust front). Suspect values were receded to missing so as not to influence the
data when determining representative diurnal profiles.
TABLE 2-1. SUSPECT METEOROLOGICAL DATA VALUES - U.S.
Suspect values
Parameter Acceptable range (frequency)
Relative humidity 5 to 100% 0(1)
Dewpoint -50 to 85° F* 87(1), 97(1)
Wind speed 0 to 50 knots 86(1), 80(1), 999 (42)
Temperature -50tollO°F 275(1)
Total cloud cover 0 to 10 tenths None
*Readers more familiar with metric units may use the factors listed on Page vii to convert units to that system.
Univariate statistics were calculated individually for each year and sample statistics were
plotted such that gross anomalies in the data could be identified. As a result of this
evaluation, it was noted that Phoenix appeared to be unusually cloudy for July 1984. Since
8
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July is of particular interest for biogenic emissions calculations, the potential for erroneous
cloud cover data was checked. A review of the Daily Weather Maps9 showed July 1984 to be
unusually wet and cloudy for the Phoenix area. When compared with data in the hourly
records, this appeared to be reasonable.
The U.S. meteorological data files included sites in Hawaii, Alaska, the Caribbean Islands,
and other overseas stations of the National Weather Service (NWS), U.S. Navy, and U.S. Air
Force. Therefore, a decision was made to remove these sites along with other sites not within
the NAPAP grid boundaries. Since the meteorological data will be spatially interpolated to
grid cells where data are not available, stations within 5 degrees latitude and longitude of the
grid boundaries were retained in the data base. The inclusion of these sites improved the
interpolation results along the grid and land boundaries. Removal of sites outside this 5
degree margin resulted in more efficient processing in the steps which follow.
In preparation for calculation of representative diurnal profiles, the three annual
meteorological files for the U.S. were concatenated into a single file. The concatenated file
was used along with the SAS UNIVARIATE procedure in the development of representative
diurnal profiles for each meteorological parameter.
Canadian Meteorological Data Processing and Quality Control
Ten magnetic tapes were read and processed to obtain hourly meteorological data for three
years: 1983, 1984, and 1985. The data for 1983 and 1984 were each contained on a single
tape, while the data for 1985 were supplied on eight magnetic tapes. The desired
meteorological data were obtained from the files using a Fortran program supplied by
AREAL which was modified for this application. Processing during the initial stage was
performed on the NCC VAX computer. Once the desired meteorological parameters were
extracted from the original data tapes, the data were transferred to the NCC IBM for further
processing and quality control checks.
Preliminary quality control checks, similar to those described for the United States, were
performed on the data to assess the completeness of the data for each year. An initial check
on the number of records for each year indicated that about twice as many records were
present in the 1985 data than were available for the other two years. A comparison of the
1985 data with that of 1983 and 1984 revealed that 131 and 137 sites were provided for 1983
and 1984, respectively, while 277 stations were available on the tapes for 1985. The stations
present in the data for 1983 and 1984 were compared with those provided for 1985 to
determine the cause of the discrepancy in the number of reporting stations. A review of the
data found that the tapes for 1985 included secondary stations which record data for less than
24 hours per day.
The primary meteorological data sites represented in the Canadian database are presented in
Figure 1-1. It should be noted that Figure 1-1 contains "cloned" meteorological stations. The
procedures and necessity for the cloned stations is discussed under Spatial Interpolation. The
applicability of these sites for use in the interpolation was evaluated while other checks were
performed on the data.
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In an effort to identify secondary sites in the 1985 meteorological data file, the number of
hours for which nonmissing data values were reported for 1985 per site were counted.
Several secondary stations were identified as a result of this analysis; however, the remaining
number of stations in the 1985 data file still exceeded the number of sites present for 1983
and 1984.
To further identify other potential secondary stations in the 1985 data file, the number of
nonmissing observations was counted for each of the desired meteorological parameters
individually. The results indicated that the data capture for wind speed and wind direction
(note that wind direction is not used for biogenic emissions calculations but is used as a
check for suspect wind speed data) was significantly higher than that of the other parameters.
The reason for this is that many stations report wind data on a 24-hour basis, and the
remainder of the data is reported for limited time spans. As a result, a more specific check
was performed on the number of nonmissing observations for temperature, cloud cover and
relative humidity.
The results of this effort provided a list of 130 stations reporting data on a 24-hour basis, and
an additional 7 which report data for most of the day. Of these 137 sites, 5 were exclusive to
1985 and the remaining 132 stations reported data in at least one of the other two previous
years. Completeness checks on the number of nonmissing observations in 1983 and 1984 did
not indicate any large data gaps. The effect of missing data on the representativeness of the
diurnal profiles was evaluated while calculating univariate statistics for the three year period.
Additional quality control checks were performed on the reported values for temperature,
cloud cover, relative humidity and wind speed for the three year period. The acceptable data
ranges used as criteria for each parameter and the suspect values noted as a result of this
check are presented in Table 2-2. Dew point values were also checked to help identify
potentially suspect relative humidity data. Twelve of the 16 suspect values were noted for
relative humidity. An additional four suspect values for dew point were also noted. The
suspect relative humidity data did not have corresponding dew point data and as such, could
not be verified. These were receded to missing. The four suspect dew point values were not
modified since they did not correspond to suspect relative humidity values and the dew point
is not used by the Canopy Model.
In preparation for calculating representative diurnal profiles, sites not within the NAPAP
borders including a five degree margin beyond the boundaries were eliminated from the data
base. The three annual Canadian files were concatenated into a single file and used with the
SAS UNIVARIATE procedure to develop representative diurnal profiles for the four
meteorological parameters.
10
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TABLE 2-2. SUSPECT METEOROLOGICAL DATA VALUES - CANADA
Suspect values
Parameter Acceptable range (frequency)
Relative humidity 5 to 100% < 4% (12)
Dewpoint -50 to 85° F 90(1), 99(1), 100(1),
114(1)
Wind speed 0 to 50 knots None
Temperature -50tollO°F None
Total cloud cover 0 to 10 tenths None
CALCULATION OF REPRESENTATIVE DIURNAL PROFILES
Hourly Surface Meteorological Data
Monthly representative diurnal profiles of temperature, cloud cover, relative humidity, and
wind speed were developed for each surface station for the United States and Canada using
three years of hourly meteorological data. The SAS univariate procedure was used to
calculate the means, medians, and modes for each meteorological parameter for each site by
month and by hour. As a preliminary check on the univariate output, a portion of the data
was printed and examined for any irregularities (e.g., a large number of hours in which there
were no observations used to calculate descriptive statistics for one or more parameters).
While reviewing the printed output data for Canada, a mode of 0.0 for wind speed was noted
for several station/month/hour combinations. To determine the frequency and extent of this
occurrence, all records reporting a mean, median, and mode of 0.0 for wind speed were
output and compared with the raw data. Thirty-eight stations indicated a high frequency (i.e.,
>12 hours out of 24) of a 0.0 mode for wind speed. Examination of the raw data indicated
that calm winds were frequently reported at these stations during the suspect periods.
In addition to descriptive statistics, the number of observations used in the statistical
calculations for each meteorological parameter and the number of missing observations by
site, month, and hour were tallied in order to identify stations with a large amount of missing
data. Statistics for all hours which were based on less than one third the possible number of
observations (where the number of possible observations is equal to n-days per month times 3
years) for any parameter were printed and reviewed.
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Table 2-3 presents the results of these checks for each meteorological parameter for the
United States data for the three year period. Forty three sites indicated statistical calculations
based on less than one third of the possible number of observations for one or more of the
desired meteorological parameters for the three year period. Nine of these sites either
initiated or terminated observations within the three year period, resulting in partial yearly
records. Thirty three of these sites reported data only during certain time spans, ranging from
6 to 15 hours per day, and usually included daylight hours.
The preliminary checks on the diurnal profiles of the means, medians, and modes for Canada
indicated very few hours of reported data at the secondary stations provided for 1985. Since
the data for these stations were not available for 1983 and 1984, statistical calculations at
these sites were based on a very limited number of observations. Therefore, prior to further
evaluation of the diurnal profiles, these stations were removed from the Canadian file. Table
2-4 contains the results of the completeness checks for each meteorological parameter for the
Canadian data for the three year period after removal of the secondary sites. Nine stations
indicated statistics based on less than one third of the possible observations or no data for one
or more of the desired meteorological parameters over the three year period. In most cases,
data were missing during the evening hours when the stations did not operate or only reported
automated wind readings.
While performing quality control checks on the Canadian diurnal profiles, it was noted that
four stations were deleted due to no match in the latitude/longitude file. Univariate statistics
by station/month/hour were calculated separately for these sites and the data concatenated to
the file containing the Canadian diurnal profiles. Latitude and longitude data for these sites
were obtained from EPA/AREAL and the GMT adjustment values were determined from an
atlas10.
Comparison of Means, Medians, and Modes
To determine which statistical parameter would be used to develop representative diurnal
profiles for each meteorological variable, the calculated means, medians and modes were
compared for each station by month and by hour using the SAS COMPARE procedure. The
COMPARE procedure allows the user to compare the values of variables based on one of
three equality criterion: relative, percent, or absolute. For the comparison of the means,
medians, and modes, the absolute method was used. Using the absolute criterion, values are
considered unequal if the absolute value of their difference [i.e., ABS (y-x)] exceeds the user
specified criterion value. For example, when comparing mean and median temperatures,
values were considered unequal if the absolute difference between the mean and the median
was greater than 5°F. Each of the equally criterion is defined under the COMPARE
procedure in the SAS Basics manual". The Criteria values were chosen subjectively for this
assessment and the COMPARE procedure was executed for the first 1,000 observations of the
data set using the following criteria values:
temperature difference > ABS(5°F)
relative humidity difference > ABS(10%)
wind speed difference > ABS(3 knots)
total sky cover difference > ABS(1 tenth).
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TABLE 2-3. NUMBER OF METEOROLOGICAL SITES WITH STATISTICS BASED
ON LESS THAN ONE-THIRD OF POTENTIALLY AVAILABLE
DATA - U.S.
Number of Sites with Statistics
Parameters Based on
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As an additional comparison, the SAS PROC PLOT procedure was used to plot diurnal
profiles of the means, medians and modes for the month of July for three sites in the U.S.
and three sites in Canada.
Examination of the preliminary comparison and plots indicated that for all variables, the mode
would not be representative as it behaves erratically and does not follow expected diurnal
patterns. This was also apparent from the diurnal plots. For most parameters, the mean and
the medians follow each other closely, with the exception of cloud cover. The preliminary
evaluation indicates that the means and medians of the other meteorological parameters follow
expected diurnal profiles.
Based on the results of the preliminary comparisons, the COMPARE procedure was executed
for all observations for the means and the medians for the United States and Canada using
modified criteria values as follows:
United States
temperature difference > ABS(6 degrees F)
relative humidity difference > ABS(10%)
wind speed difference > ABS (3 knots)
total sky cover difference > ABS(3.5 tenths)
Canada
temperature difference > ABS(5 degrees F)
relative humidity difference > ABS(10%)
wind speed difference > ABS(3 knots)
total sky cover difference > ABS(3 tenths).
The results of this analysis indicated that the mean and the median for wind speed and
relative humidity are very close. For temperature, the mean and the median are also fairly
close. However, the median is more representative of the central tendency of a parameter as it
is not affected by extreme values (the median may be affected by the frequency of occurrence
of extreme values but not by the magnitude of the extremes themselves). Of all statistical
measures of central tendency, the mean is most affected by extreme values in a population
sample. For total sky cover, the mean shows a smoother transition from hour to hour, which
is more desirable for a representative diurnal profile. Therefore, median values were
employed for temperature, relative humidity, and wind speed, and mean values were used for
sky cover.
Quality control checks on the representative diurnal profiles generated revealed that in some
cases a monthly median wind value of 0.0 was present in the output dataset. Since a 0.0
value is not considered representative and results in slightly negative interpolation results, all
occurrences of median winds of 0.0 were replaced with the mean wind speed for that hour.
The Barnes interpolation routine may produce negative values under certain conditions. The
first pass through the grid produces values for every grid point based on weighting of nearby
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To facilitate testing of the routine, the input data and interpolation results were output in an
array with each array column and row corresponding to the subgrid columns and rows. Using
this method, the original data could be displayed along with the interpolation results from the
lower bound, upper bound and the differences resulting from subtracting the lower bound
from the upper. A section of the NAPAP grid, with the lower left corner located near Little
Rock, AR and the upper right corner located near Pittsburgh, PA, was used in the series of
tests. This subgrid contains 35 rows and 50 columns.
Temperature values were initially used as input data for the tests. Gamma was set to 0.3 in
the first test while D0 values of 0.1 and 0.6 were tested. The range of differences for the
interpolation tests using D0 = 0.1 and D0 = 0.6 was -1.8 to 2.3 degrees F. This 4.1 degree F
range represents nearly 5% of the input values. Approximately 5% of the cells had
differences exceeding plus or minus 1 degree F with the remaining cells having values less
than plus or minus 1 degree F.
In the second test, D0 was set to 0.5 while gamma values of 0.3 and 0.5 were employed.
Differences ranged from plus to minus 0.2 degrees F. More than 95% of the cells had
differences of less than 0.05 degrees F.
From these results, it was evident that interpolation results are more sensitive to changes in
D0 than gamma. A value of 0.5 for D0 when used with either of the above values of gamma
produces a reasonably smooth field of interpolated values.
Testing was then expanded to the entire NAPAP grid for all four meteorological parameters.
A plot of test results showed the data sparse areas of northern Canada contained regions with
no interpolated data. Several cells in the Rio Grand valley of Texas also had no coverage.
Since biogenic emissions could not be calculated without the meteorological data, the
following strategy was implemented to provide complete interpolation coverage. Station data
were duplicated and assigned as data points in nearby grid cells in the areas lacking coverage.
The assignment of duplicate station data was kept to the minimum necessary to ensure that
the greatest number of uncovered cells were provided interpolation results.
The duplication of meteorological data and false location assignment or "cloning" was
performed a total of nine times to provide coverage over land areas. Figure 2-1 presents the
coverage provided after duplication/relocation was performed. Prior to the data duplication.
candidate sites were scanned by parameter by hour to assure complete records. Data for only
five sites was required to accomplish this coverage. The region south of Hudson's Bay
required four duplicates of data from one site to provide complete coverage in that area. A
second site in northeastern Quebec was duplicated twice to cover far north-central Quebec.
Three other sites, located in northwestern Manitoba, northern Saskatchewan and southwestern
Texas, were duplicated once.
The climatology of northern Canada is quite uniform with the exception of areas near the
coast of Hudson's Bay. When deciding on the location of duplicate sites for application to
the uncovered region in northern Quebec, it was felt that duplicating either of the sites on
Hudson's Bay could propagate coastal influences to inland locations. It was preferable to
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00
Figure 2-1. Plot of Interpolation Test Results.
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data points. The second pass uses the first pass results to perform a simple bilinear
interpolation at each grid cell using the four surrounding grid cells. This interpolation may
result in positive and negative adjustments to the first pass results. A grid cell having an
initial value of zero and adjacent cells with non-zero values could become slightly negative
after the bilinear interpolation is complete. This condition was found to occur in data sparse
regions. This problem was not observed for any of the other meteorological parameters.
Final Data Processing
To prepare the meteorological data for interpolation, the diurnal profiles for the U.S. and
Canada were concatenated into a single file. Column and row numbers were calculated for
1/6 degree latitude by 1/4 degree longitude grid cells from the latitude and longitude of each
station using the NAPAP grid origin (i.e., 1,1 = 25 degrees N latitude and 125 degrees W
longitude). Meteorological parameters were converted to units for compatibility with the
Canopy Model (i.e., temperature = degrees C, relative humidity in fractions [e.g., 0.50], and
wind speed in m/s). (Sky cover data is not directly input to the Canopy Model. Use of this
data will be discussed under SOLAR RADIATION.) In addition, hourly values were adjusted
to GMT and missing values were coded to -99.0. Prior to output, records corresponding to
representative hourly values in which statistics were based on less than 80% of a single
month in which only one year of data were available (i.e., n < 24) for all meteorological
variables were deleted as these would not be considered representative. The data were sorted
by month and hour and output to an EBCDIC file for interpolation.
SPATIAL INTERPOLATION
The Barnes interpolation technique12-13 was investigated as a means of providing
meteorological data for all cells in the NAPAP grid. This technique has been widely used for
temporal and spatial interpolation of meteorological data. It has found wide acceptance for
two major reasons; it is a computationally simple algorithm, thus minimizing computer
program execution times, and it allows the user to adjust key parameters (i.e., the
convergence factor and the initial resolution) until results are considered acceptable. The
initial resolution (D0) is a dimensionless measure of the first pass response. The interpolation
routine employed allowed the user to select a value for this variable in order to produce the
desired results. The convergence parameter (gamma) is a factor which controls the degree of
convergence between the observed field and the results of the second pass interpolated field.
The version of the Barnes algorithm employed in this study, modified in 1973, performs the
interpolation in two steps. The user must first define a grid area, usually a subsection of an
area for which data are available. The program initially determines the number of data points
in the selected area and measures the distance between every pair of data points to determine
data spacing. The program then loops over every grid point, measuring the distance to each
data point to determine a weight for each data point. The weight is calculated by a Gaussian
relation of the form
W=*xp[-(ia/k0)]
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where W is the weight, r is the distance from data to grid point, and ko is the weight factor.
The weight factor is defined by the relation
k0=(-logD0)(avespa)/(2)(3.1416)
where D0 is the initial resolution and avespa is the average data spacing. The average
spacing, calculated by the program, is the average distance between data points (km) in the
grid area. It is calculated by summing the distances between all data points and dividing by
the total number of data points. Should a data point be coincident with a grid point, the
weight assigned to that grid point is the maximum value of 1. When 2 or more data points
fall within a grid cell, an average of the values is taken. As grid points further removed from
the data are evaluated, the weight drops exponentially.
This process of assigning weights is conducted for all data points in the defined area. Grid
points will typically fall within the region of influence of many data points. The routine then
incorporates the weight factors as it calculates a value for each grid point. The spatial extent
of the region of influence is dependent on the data spacing and user specified parameters.
When the distance between a data point and an interpolation point increases, the weight for
that data point-interpolation point pair decreases. The user is allowed to define maximum
distances between data points and interpolation points that will be considered to ensure
complete coverage and representative interpolation values while maintaining computational
efficiency. This approach is most valuable when interpolating over a large region that has
many data points.
To facilitate computing of interpolated values, grid points beyond a user specified distance of
a data point are not considered as the weight becomes insignificant. This approach can be of
value when interpolating for a grid with many points.
At the conclusion of the first pass, all grid points have been assigned values. The second
pass uses the results of the first pass to perform a simple bilinear interpolation using the four
surrounding grid points. This interpolation will produce an adjustment to the first pass
yielding smoother final results. The user can select values for the convergence factor
(gamma) and initial resolution (D0). Because the two parameters may be varied, a series of
tests may be required to assess the effects of each on the interpolated values.
In reviewing the related literature provided by AREAL14, a recommended range of 0.3 to 0.5
for convergence factors was used in the tests. By definition, D0 ranges from 0 to 1. A value
of 0.1 for D0 was believed to provide a reasonable lower bound for testing purposes. In order
to compare results for the lower and upper bounds for both variables, one variable was set to
a constant value while the second was run at both upper and lower bounds. For D0 values of
0.7 and greater, a floating point error condition was encountered. The system, however, took
"standard corrective action" and results were obtained. The interpolation results for these
values of D0 were not desirable. Results were not considered to be smoothed sufficiently
with relatively large gradients present in the areas tested.
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duplicate a site in northern Quebec, located further inland, and relocate it to the west. In this
way, any coastal effects of the bay would diminish with increasing distance from the water.
Three steps were performed in order to provide evidence of complete interpolation coverage.
First, the composite 3 year meteorological data file was scanned to produce the number of
sites reporting data for at least one of the four parameters for each hour of each month. This
tally yielded a maximum of 399 and a minimum of 384 sites before the nine false sites were
incorporated. A plot of the entire grid area was produced for the time period reporting the
minimum number of sites. This plot demonstrated complete coverage over land areas.
The second method of addressing completeness of coverage was performed by scanning the
interpolated data files for cells with values of 0.00, indicating no coverage. (Note: this test
could not be performed for temperature during cold months as 0.00 could be valid data. It is
highly unlikely that any of the other three parameters could have valid data with a value of
0.0. In order to test temperature, warm months were scanned.) The time period indicating
the greatest number of empty cells was plotted, and indicated complete land coverage. The
greatest and least number of empty cells are 14,465 and 14,072, respectively. These cells are
located over the water as can be seen in Figure 2-1. This reasonably narrow range (393 out
of 64000) also supports the premise that coverage is adequate and consistent over time.
The third check scanned twenty sites in northern Canada which singularly provide
interpolation coverage to a land area. Coverage in these areas is most vulnerable to missing
data because of the interpolation dependence on a single site. This final check, done by
parameter by hour, demonstrated complete data capture for sites in the data sparse areas,
precluding additional cloning.
With the question of coverage adequately addressed, the interpolation runs were executed.
Based on the results of the preliminary tests, values of 0.5 for initial resolution and 0.3 for
gamma were used in the interpolation runs. This exercise required 48 computer runs - 4
parameters for 12 months each, with each run processing 24 hours of data. Grid plots of
interpolated meteorological data for select months are presented in Appendix C.
SOLAR RADIATION
Monthly average hourly solar radiation values for each grid cell were calculated as a function
of latitude, longitude, day of the year, and hour of the day using existing computer software
(SOLENGY.FORT) developed by AREAL. Minor modifications to the software were
required for execution on the NCC IBM and for the NAPAP grid boundaries. A new solar
radiation algorithm was provided by EPA/AREAL and incorporated into SOLENGY.FORT to
calculate the solar insolation for this project. This algorithm was obtained from the Urban
Airshed Model Emissions Preprocessor System15. The new software provides calculations of
hourly total and visible radiation for use in the Canopy Model (note that visible is estimated
as half of the total radiation). The effect of the new algorithm resulted in reduced emissions
magnitudes for isoprene relative to other studies completed previously (since the modified
Tingey curves for isoprene are based on visible radiation). Additionally, the new algorithm
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allows a more gradual increase and decrease in solar insolation near sunrise and sunset. A
listing of the SOLENGY.FORT Fortran code is provided in Appendix D.
The solar flux over a twenty-four hour period for the midpoint day of each month was
calculated to represent the average diurnal solar insolation for each month. Representative
samples of the output data for each season were read into SAS and checked for
reasonableness (e.g., order of magnitude and relative flux from season to season and diurnally
from hour to hour) and were compared to previous output from SOLENGY.FORT.
Cloud cover data were used to adjust the clear sky total and partial solar intensities for
attenuation by cloud cover to more closely represent actual conditions present in the
atmosphere. Gridded cloud cover data for each month and hour output from the spatial
interpolation routine were merged with gridded solar insolation data for each month and hour.
The algorithm used for attenuation was obtained from Kaston and Czeplak (1980)16:
attenuated solar rad. = solar rad. x [1 + C * (skyr0)],
where C = -0.75, D = 3.4, and skyt is the fractional total sky cover (e.g., 0.4). Quality
control checks were incorporated into the attenuation software to flag and print out any
potential occurrences of missing cloud cover or solar radiation data and any interpolated
values less than zero. Additionally, portions of the attenuated solar radiation data were
printed and checked for anomalies and correspondence with cloud cover data and expected
diurnal behavior. No problems were found with the data.
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SECTION 3
CALCULATION OF BIOGENIC HYDROCARBON EMISSIONS
Methodologies for calculating emission rates of biogenic hydrocarbons and NOX require
estimates of vegetation density and cover, seasonal variations of biomass growth, emission
rates for vegetative classes, and meteorological parameters such as temperature and solar
intensity. The previous section describes the methodology used to develop monthly
representative diurnal profiles for the required meteorological parameters. This section
describes the methodology and data bases used to estimate monthly gridded leaf biomass,
land cover data, and emissions. Aggregation of hourly gridded emissions to the county and
State levels for monthly, seasonal, and annual temporal scales is also discussed in this section.
Software used to calculate gridded biomass and the Canopy Model used for this project were
obtained from EPA. New temperature correction algorithms for soil NO,, provided by
NOAA. were incorporated into the Canopy Model for this project.
Emissions for each vegetation class are calculated by multiplying gridded leaf biomass and
land use data (hectares) by the compound specific emission factor for each vegetative type.
Using the Canopy Model, emission correction factors are calculated to adjust emissions for
environmental factors such as temperature, solar insolation, leaf orientation, and location in
the canopy. A description of the biomass data, methodology and software has been
documented by EPA and is provided in Appendix B. A brief description is also provided
here for completeness.
CALCULATION OF BIOMASS
Data from Oak Ridge National Laboratory's Geoecology Data Base17 and the LANDSAT and
Land Use/Cover Inventory18 form the basis for the gridded biomass and coverage data for the
United States and Canada. The Geoecology Data Base contains leaf biomass and noncanopy
land use data at the county-level for various crop types, tree species and urban trees. County-
level data were allocated to the grid level using the gridded LANDSAT Land Use/Cover
Inventory previously developed for the NAPAP project. This land use/cover inventory
represents data collected in the middle to late 1970s. Gridded leaf biomass and land use data
for Canada were based on the LANDSAT Land Use/Cover Inventory and on Vegetation,
Land Use, and Seasonal Albedo data sets.19 Agricultural lands for Canada were allocated to
specific crop types by EPA.
Vegetative classes used for biogenic emissions calculations include: natural forested
vegetation (specifically, oak, coniferous, and other deciduous); other natural vegetation such
as scrubland and grasslands; and agricultural crops (alfalfa, barley, corn, cotton, hay, oats,
peanuts, potatoes, rice, rye, sorghum, soybean, tobacco, wheat, and miscellaneous crops). The
three forest classes (oak, coniferous, and other deciduous) are each disaggregated to four
biomass classes (high isoprene deciduous, low isoprene deciduous, nonisoprene deciduous.
and nonisoprene coniferous) to account for understory vegetation and mixed forest types.
Canopy biomass and noncanopy land cover data are provided at the county-level in the
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Geoecology Data Base. The LANDSAT and Land Use/Cover Inventory is used to spatially
allocate these county level data to 1/4 longitude by 1/6 latitude grid cells. A more detailed
description of the methodology used to adapt these data bases for use in the biogenics
emissions inventory has been documented by EPA in Appendix B.
The gridded leaf biomass and land use data are input to a SAS program (BIOMASS.SAS)
created at EPA's Atmospheric Research and Exposure Assessment Laboratory (AREAL). The
SAS code for this program is provided in Appendix D. This program uses the gridded leaf
biomass and land use areas, biomass density factors, and growth factors to create a file of
episode (e.g., month) specific leaf biomass and land use data which are subsequently used to
calculate biogenic emissions estimates. Biomass for the noncanopy classes are not calculated
directly since emission rates for these classes are a function of surface area instead of biomass
amounts.
Seven input files are required for execution of BIOMASS.SAS: an episode file containing the
month desired for biomass estimates: gridded growth factors for noncanopy vegetation;
gridded biomass factors for canopy vegetation; a file containing the user specified grid origin
and boundaries; and three files containing the gridded leaf biomass and land use areas for
canopy, noncanopy and urban tree vegetation. Files containing the growth and biomass
factors and gridded biomass areas were provided by EPA. The episode and grid origin files
were created for this application.
Previous efforts indicated a possible problem in the canopy biomass factors originally
provided by EPA (e.g., a high percentage of isoprene and monoterpene emissions during the
winter). During the colder months, it is assumed that coniferous species retain one third to
one half of their summer foliage. A review of the biomass factors by EPA revealed that
deciduous and oak species within coniferous forests had been assigned foliage during the
winter. Conversely, coniferous species in oak and deciduous forests were assigned no foliage
during the winter. The new factors, used in the current inventory, have been corrected such
that coniferous species in oak and deciduous forests are assigned foliage during the winter
months and all oak and deciduous species have no foliage during the winter months.
BIOMASS.SAS execution results in three output files which are used in conjunction with
species specific emission factors to calculate biogenic hydrocarbon and NOX emissions. These
include the canopy biomass, noncanopy areal coverage, and urban tree coverage.
BIOMASS.SAS output for each month was checked by printing the biomass for specific
geographic areas for each month and comparing the changes in biomass from month to month
with expected monthly or seasonal growth patterns.
In a previous work assignment, quality control checks were performed on the leaf biomass
and land use areas input to BIOMASS.SAS20. The focus of the quality control effort was to
check the data for reasonableness of crop, forest and urban area distributions. The analysis
utilized grid plots, statistical parameters calculated for each vegetative species, and various
literature sources such as almanacs and data from the U.S. Bureau of the Census and
Department of Agriculture. For canopy vegetation, additional reference materials were not
available in the required time frame, and therefore, these were not compared with additional
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data sources. The details of this analysis have been previously documented20. A brief
summary of the findings is presented below.
A comparison of the noncanopy vegetation distribution with selected reference information
indicated that for a few crop types, the presence of specific crops in various States did not
correspond to data derived from the Geoecology Data Base. For example, Agricultural
Statistics 798S21 indicated that rye is planted in States such as Oregon, New Jersey, Michigan,
Oklahoma, Virginia, and Texas. The grid plots generated from the land use areas however,
do not indicate this growth. Quality control checks indicated that the urban area distributions
were valid based on urban geographic locations. The results of this analysis were submitted
to EPA.
CALCULATION OF CORRECTION FACTORS
Emission rates of biogenic hydrocarbon and NOX from soils are dependent on temperature and
for isoprene, incident solar radiation intensity as well. Researchers at Washington State
University have developed a Canopy Model for forested areas. In forested areas, the Canopy
Model corrects for leaf temperature using the heat energy balance computed for representative
levels at different heights in the forest canopy. The model applies factors to calculate leaf
temperatures and leaf exposures to sunlight in eight representative layers from the forest floor
through the height of the canopy. In addition to temperature and solar radiation, wind speed
and relative humidity are used for calculating the heat energy balance in the canopy.
Monthly representative diurnal profiles of ambient temperature, solar radiation, wind speed
and relative humidity were developed for use with the Canopy Model. The methodology for
development of these data is detailed in Section 2 of this report. A SAS version of the
Canopy Model (CORRECT2.SAS) was provided by EPA for use with the meteorological data
profiles developed for this project. Additionally, based on additional field measurements, new
soil NOX emissions and temperature correction algorithms were provided by the National
Oceanic and Atmospheric Administration (NOAA) in Boulder, CO and incorporated into the
Canopy Model for this project. The SAS listing for CORRECT. SAS is provided in
Appendix D.
CORRECT2.SAS utilizes gridded hourly meteorological data and calculates emissions
correction factors for noncanopy, canopy, and urban tree classes. The correction factors are
used to adjust mean emission rates, which are based on a temperature of 30°C, to leaf
temperatures (which are in turn determined from ambient temperature). Additionally for
isoprene, correction factors adjust emissions for the intensity of solar radiation (i.e., visible
radiation). Noncanopy correction factors use hourly temperature data and temperature
relationships from Tingey22 to calculate correction factors for monoterpenes, alpha-pinene, and
unknown hydrocarbons. For isoprene, correction factors use hourly temperature, solar
insolation data, and a modified version of the Tingey curves22 to calculate correction factors.
Correction factors for forested areas use hourly temperature, wind speed, and relative
humidity data to calculate correction factors at eight levels in the canopy using a heat energy
balance. In addition, isoprene correction factors are also adjusted for solar intensity based on
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solar insolation data and location in the canopy using a modified version of the Tingey
curves.
The Canopy Model algorithms are based on the assumption that ambient meteorological
conditions are representative of the top of the canopy. The basis for the Canopy Model
calculations is the leaf radiation balance of a typical leafs surface using an iterative approach
downward through the canopy. The total solar radiation intensity is decreased exponentially
downward through the canopy as a function of the biomass distribution. The leaf temperature
is calculated by a radiative balance algorithm which uses ambient temperature, total radiation,
relative humidity, and wind speed and is used with Tingey's equations to calculate the
correction factors.
Two files are output for use with the biomass and land use data to calculate biogenic
hydrocarbon and NOX emissions: a file of canopy emission correction factors and a file of
noncanopy emission correction factors. Correction factors for a limited geographic area for
each month were printed and checked to assure they followed expected diurnal and seasonal
patterns.
CALCULATION OF BIOGENIC HYDROCARBON EMISSIONS
The final step for calculation of biogenic hydrocarbon and NOX emissions (RADMBIO.SAS)
uses the month specific hourly corrected emissions factors output by CORRECT2.SAS and
the monthly leaf biomass and land use data generated by BIOMASS.SAS to calculate gridded
hourly biogenic hydrocarbon and NO, emissions for isoprene, monoterpenes, alpha-pinene,
unknown hydrocarbons, NO and NO2. The SAS source code listing for RADMBIO.SAS is
presented in Appendix D. RADMBIO.SAS first calculates standard gridded hourly canopy
and noncanopy biogenic emissions. The standard conditions are adjusted for ambient
conditions using the correction factors. The canopy emissions are calculated by multiplying
the layered biomass by canopy specific emission factors. Grassland NOX emissions are
calculated with the noncanopy emissions. The calculated emissions for each vegetative
species are summed together such that the resultant output file contains the total emissions of
isoprene, monoterpenes, alpha-pinene, unknown hydrocarbons, NO, and NO2 for each grid
cell. The final units of the output emissions data are grams per second which represents the
emission rate of compound for a specific hour for a given month for that grid cell.
RADMBIO.SAS outputs a single file containing the combined canopy, noncanopy, and urban
tree biogenic emissions in a format consistent for input to the Regional Acid Deposition
Model. Emissions for selected geographic areas were printed and evaluated to assure they
followed expected diurnal and seasonal patterns. Additionally, calculated emissions were
compared with hourly correction factors to assure they followed similar behavior patterns.
SPATIAL AND TEMPORAL AGGREGATION OF EMISSIONS DATA
Hourly gridded emissions (grams per second) were generated for input to regional models
such as RADM. Larger temporal and spatial scales however are required for use in emissions
assessment evaluations comparing the magnitudes of anthropogenic and natural sources.
24
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Therefore, the biogenic emissions estimates were aggregated spatially to the county and State
levels (province level for Canada) and temporally to monthly, seasonal, and annual levels.
Spatial aggregation of the data used a data file obtained from EPA containing the gridded
land areas by county for the U.S. and by province for Canada.
Temporal aggregation to the monthly level required multiplying the resultant emissions for
each hour output as grams per second by 3600 to arrive at grams per hour. Hourly emissions
were summed over the twenty-four hours in each representative day for each month and
multiplied by the number of days in each month. Monthly emissions, reported in grams, were
converted to tons (i.e., short tons) to be consistent with anthropogenic VOC data developed
for the NAPAP inventory. Seasonal emissions were obtained by summing over the three
months which comprise each season. Seasonal values were summed to arrive at annual
emissions values (tons/season). Later in this report, emissions data are also presented in
teragrams and in kilograms/hectare for comparisons with other biogenic inventories.
Spatial aggregation of emissions to the county level required assigning the gridded emissions
data to the appropriate State and county. This was accomplished using an area file provided
by EPA which contained the land area of each grid cell in each county. Several adjustments
to this file were required for processing and for compatibility with the NAPAP emissions
inventory. These are summarized below.
For use with the NAPAP emissions inventory, the grid number and FTPS codes provided in
the area file had to be converted to column and row numbers and to NEDS (AEROS) codes,
respectively. Additionally, modifications to the file for Massachusetts and Virginia were
required for compatibility with the NAPAP inventory. Specifically, Massachusetts counties
were apportioned to Air Pollution Control Districts and Virginia Independent Cities were
incorporated into the FIP AEROS file. Other minor modifications to the FTP AEROS and area
files were made to discrete counties to assure compatibility.
The area file was used to calculate the fraction of each grid cell in each county for the U.S.
and to calculate the fraction of each grid cell in each province for Canada. The gridded
biogenic emissions were multiplied by the fraction of each grid cell in each county/province
to arrive at the biogenic emissions for each county (province)/grid combination. Grid cells
for each county were summed to arrive at county-level emissions. County-level emissions for
each State were summed to obtain State level emissions.
Quab'ty control checks of the spatially aggregated emissions data revealed that species specific
and total biogenic hydrocarbon emissions increased slightly (<2%) as a result of the grid to
county aggregation. To determine the possible source of this anomaly, the calculated land
area fractions for each county were summed. Any fractions which summed to more than one
at the county level were output and printed to six significant digits. Three hundred seventeen
counties indicated fractions summing to greater than one. However, the excess in all cases
was less than six significant digits.
25
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SECTION 4
NATURAL PARTICULATE AND BIOGENIC EMISSIONS DATA
PARTICIPATE MATTER
Naturaljjarticulate matter emissions data were calculated for thme-^ource_caiegories^ in the
1985 NAPAP Emissions Inventory (Version 2).6 These^categories include .unpaved road just,
wind erosion (wind blown dust), and dusLdeyils. The methodologies used to calculate county
level annual and resolved gridded hourly paniculate emissions were documented with the
1985 NAPAP Version 2 inventory.6 Improved annual emissions estimates for county-level
unpaved road dust were developed for the United States following the completion of the 1985
NAPAP Emissions Inventory (Version 2). The improvements resulted from modifications of
the assumptions used to specify the emission factors and improvements in the methodology
applied to allocate State totals to the county-level. The change in the emissions calculation
methodology involved the addition of a plume depletion factor in the emission flux algorithm.
The plume depletion factor was implemented to account for the large fraction of particles less
than 10 urn that fall out within several feet of the roadway and therefore are not considered to
be released into the atmosphere.23 The plume depletion factor used in these analyses was 0.1,
which is based on measurements that indicate that 90% of the total mass of road dust
gravitationally settles very soon (within minutes) after the road surface disturbance.
The updated annual county-level unpaved road particulate matter data for the United States
were spatially resolved to the grid level, speciated into component alkaline fractions and
temporally resolved to the hourly level for a typical weekday, Saturday, and Sunday in each
of the four seasons. The allocation was accomplished using the Flexible Regional Emissions
Data System (FREDS).5 Tabular summaries of the revised United States natural particulate
data are presented in Appendix A. An index of the pollutant identification names used as
column headings in these tables is also provided in Appendix A. Tables A-l, A-2 and A-3
list the data totals by State, EPA region and source category respectively. These data
supersede the data contained in Tables A-7, A-14, and A-26 in the 1985 NAPAP Emissions
Inventory report.6 Tables A-4 and A-5 for Canadian natural source particulate emissions
correspond to Tables A-21 and A-31 of the NAPAP inventory report. The Canadian
methodologies for estimating unpaved road dust emissions were not affected by the changes
in the United States' methodology. The data in the Canadian emissions summary tables,
therefore, were not modified from the Version 2 inventory report and are included here only
for completeness.
Table 4-1 lists the revised tape totals for the combined U.S. and Canadian natural source
particulate matter data. The data in this table update Table 9-21 from the Version 2 inventory
report. It should be noted that the sum of the tape totals in Table 4-1 do not correspond to
26
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TABLE 4-1. TAPE TOTALS FOR COMBINED U.S. AND CANADIAN NATURAL
PARTICULATE SOURCES
SCENARIO
01
02
03
04
05
06
07
08
09
10
11
12
Total 01
(TSP)
187,608.01
178,459.87
167,843.65
242,597.35
232,397.34
215,857.30
268,629.95
257,157.65
241.426.12
210,446.00
200,164.98
185,531.81
Total 06
(Cal)
131.86
129.75
127.49
157.09
154.70
151.43
148.45
145.78
142.54
136.69
134.30
131.33
Total 10
(Mg2)
251.56
250.02
265.44
303.60
302.53
299.91
285.16
284.04
281.72
254.06
253.06
250.85
Total 18
(PM1)
21,414.14
18,692.22
16,171.30
24,561.52
21,526.73
18,613.21
27,536.91
24,124.32
20,916.33
24,041.27
20,982.84
18,096.99
Total 20
(PM3)
24,183.44
22,934.11
30,106.20
30,037.22
28,644.80
27,087.52
33,001.41
32,435.94
30,801.23
26,387.67
24.984.84
23,498.16
Total 2,588,120.03 1,691.41 3,281.95 256,677.78 334,102.54
Scenarios refer to day types in the 1985 NAPAP Modelers Emissions Inventory (Version 2).
The scenarios represent the typical weekday, Saturday and Sunday in each of the four seasons.
The scenarios run from number 01 which is the winter weekday, through 12 which is the fall Sunday.
The column headings refer to the species represented: TOTAL 01 (TSP) represents total suspended paniculate;
TOTAL 06 (Cal) represents calcium in the 0.0 - 2.5 micrometer diameter size range; TOTAL 10 (Mg2) represents
magnesium in the 2.5 - 10.0 micrometer diameter size range; TOTAL 18 (PM1) represents total paniculate in the
0.0 - 2.5 micrometer diameter size range and; TOTAL 20 (PM3) represents total paniculate in the 6.0 - 10.0
micrometer diameter size range.
27
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the annual totals presented in Appendix A as each of the scenario totals represents emissions
for a given day type (weekday, Saturday, or Sunday) in each season. To arrive at an annual
total, emissions for each of these day types would have to be multiplied by the number of
occurrences in each season (e.g. 5 weekdays x 13 weeks per season).
The revised United States emissions estimates for natural total suspended paniculate (TSP)
emissions from unpaved roads is 36,922,642 TPY. The original U.S. total for unpaved road
emissions in the 1985 NAPAP Emissions Inventory (Version 2) was 35,775,602 TPY (short
tons). A detailed description of the revised methodology used in the development of the
current version of the county-level natural paniculate emissions estimates is presented
elsewhere.23 As a result of the changes in the emissions calculation methodology for unpaved
road dust, the total natural paniculate emissions for the U.S. increased from 50,253,334 TPY
reported in the original NAPAP data6 to 51,400,375 TPY reported in this document.
Canadian natural paniculate emissions remain unchanged from the NAPAP Version 2
Emissions Inventory.
BIOGENIC HYDROCARBON AND GRASSLAND NOX EMISSIONS
Overview of Emissions Data
Hourly gridded biogenic emissions estimates were spatially aggregated to the county- and
State-levels for each month and season and annually as discussed in Section 3. Seasonal and
annual emissions totals for the individual hydrocarbon compounds and NOX species are
presented for both the United States and Canada in Table _4r2^ These data are presented in
teragrams (Tg) for comparison with previous biogenic hydrocarbon emissions data generated
by Lamb, et alu. Tabular summaries of seasonal biogenic emissions estimates for the United
States by State in short tons are presented in Tables A-6 through A-9 for winter, spring,
summer, and autumn, respectively. Similar tables for Canada by province are presented in
Tables A-10 through A-13~
The relative contributions of biogenic hydrocarbons and grassland NOX emissions by season
are shown in Figures 4-1 and 4-2 for the United States and Canada, respectively. Grassland
NOX emissions which are dependent not only on the growing season but also on temperature
are zero for Canada in the winter and are very small for the spring. Figure 4-1 for the U.S.
shows that approximately 50% of the biogenic hydrocarbon and natural grassland NOX
emissions occur in the summer months, approximately equal amounts in the spring and fall
and much lower amounts in the winter. Figure 4-2 for Canada also shows that approximately
half of the biogenic hydrocarbon emissions occur in the summer months with almost equal
amounts in the spring and fall and much lower amounts in the winter. Grassland NO^
emissions for Canada (Figure 4-2) occur mainly in the summer (84%) with most of the
remainder occurring in the fall (16%) and less than 0.1% (1.83 tons) occurring in the spring.
28
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TABLE 4-2. BIOGEN1C HYDROCARBON AND GRASSLAND NOX EMISSIONS
SUMMARY
U.S. Sources
isoprene
alpha-pinene
other monoterpenes
unknown hydrocarbons
total hydrocarbons
Grassland NO '
Grassland NO2
total NOX
Canadian Sources
isoprene
alpha-pinene
other monoterpenes
unknown hydrocarbons
total hydrocarbons
Grassland NO *
Grassland NO,
Winter
0.02
0.23
0.22
0.27
0.74
2.4 x lO'3
2.2 x JO"4
2.6 x 10'?
0
0.14
0.12
0.12
0.38
0
0
Seasonal Emissions
Spring Summer
0.72
0.68
0.67
1.83
3.90
0.043
4.0 x 10'3
0.047
0.02
0.38
0.36
0.41
1.17
1.5 x 10'6
1.3 x 10'7
2.36
1.50
1.54
4.64
10.04
0.12
0.01 4.4
0.13
0.38
0.93
0.95
1.51
3.77
2.5 x 10-3 5.0
2.3 x 10-4 4.6
(Tg)
Fall
0.69
0.70
0.69
2.04
4.12
0.048
x 10'3
0.052
0.08
0.40
0.38
0.51
1.37
x 10'4
x 10'5
Annual
3.79
3.11
3.12
8.78
18.8
0.21
0.019
0.23
0.48
1.85
1.81
2.55
6.69
3.0 x 10'3
2.8 x 10'4
total NO,
1.6 x lO'6 2.7 x 10'3 5.5 x lO"4 3.3 x 10'?
29
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TOTAL HYDROCARBONS
SUMMER
53X
SPRING
21%
WINTER
4X
AUTUMN
22X
U)
O
GRASSLAND NOX
SUMMER
56X
SPRING
20%
WINTER
IX
AUTUMN
sax
Figure 4-1. Seasonal Distribution of Total Biogenic Hydrocarbon and Grassland NOX for the United States.
-------
TOTAL HYDROCARBONS
SUMMER
56X
SPRING
18%
WINTER
EX
AUTUMN
SOX
GRASSLAND NOX
SUMMER
B4X
AUTUMN
16X
Figure 4-2. Seasonal Distribution of Total Biogenic Hydrocarbon and Grassland NOX for Canada.
-------
Figures 4-3 and 4-4 are pie charts that show the percent of total hydrocarbon represented by-
each of the hydrocarbon species in the four seasons for the United States and Canada,
respectively. The data for the United States show a much lower contribution of isoprene in
the winter months relative to the other seasons. Isoprene emissions result primarily from
deciduous tree species in forest canopies, and therefore the contribution is lower in winter
months relative to the other seasons since deciduous biomass is assumed to be zero between
the first and last frost dates. The relative Figure 4-3 contribution of isoprene to the total
biogenic hydrocarbon is nearly the same for the spring and fall, greater for summer and a
minimum in the winter. Isoprene emissions from deciduous trees are dependent on the
incident solar radiation intensity and therefore have a maximum emission rate with warm
temperatures and maximum solar intensity which occur during the summer months.
The contribution of alpha-pinene and other monoterpenes to the total hydrocarbon emissions
is higher in the winter months relative to the other seasons in the United States, while the
relative contribution of alpha-pinene and other monoterpenes is similar throughout each of the
other three seasons. High winter alpha-pinene and other monoterpenes result from the large
contribution by coniferous tree species, especially in the south where the climate is relatively
moderate during the winter months.
The distribution of species for Canada by season exhibits a different pattern as is evident
from Figure 4-4. The Canadian data show no contribution of isoprene to the total biogenic
hydrocarbon emissions in the winter. The maximum isoprene contribution to total biogenic
hydrocarbon occurs in the summer, as would be expected, followed by the fall and then
spring. The relative distribution of alpha-pinene and other monoterpenes remains relatively
constant over the winter and spring in Canada and decreases in the summer and fall as the
contribution of isoprene and unknown hydrocarbons to total biogenic hydrocarbon increases.
This trend is similar to that observed in the United States where the contribution of alpha-
pinene and other monoterpenes to total biogenic hydrocarbons is higher in the winter months
than in the summer. During the spring and fall however, the relative contribution of alpha-
pinene and other monoterpenes to total hydrocarbon in Canada is higher than in the U.S. as
more deciduous foliage is present in the U.S. in the fall and spring.
Biogenic hydrocarbon emissions calculated for this project were compared with those
developed by Lamb, et allj. The first generation biogenic emissions inventory was developed
during the earlier portion of the NAPAP study1. For this inventory, emissions data developed
by Zimmerman24 were used to determine arithmetic mean emission rates for isoprene, alpha-
pinene, and other hydrocarbons. Emissions were adjusted to temperature and light intensity
using the Tingey relationships.22 Land use and climatic data were obtained from the
Geoecology Data Base.17 Mean county monthly temperatures were used and 15 hours of
daylight was assumed for the summer and 9 hours for the winter. For deciduous and
noncanopy species, growth was assumed to occur between the last and first frost dates.
For the second generation inventory, data from a number of field and laboratory experiments
were used to develop emission rate algorithms for isoprene, monoterpenes, and other
32
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SEASON-WINTER
SEASON-SPRING
ALPHA-PINENE
31X
ALPHA-PINENE
17%
OTHER MONOTERP.
30%
OTHER MONOTERP.
17%
ISOPRENE
3%
ISOPRENE
19%
UNKNOWN HC
36%
UNKNOWN HC
47%
SEASON-SUMMER
ALPHA-PINENE
15%
SEASON-AUTUMN
ALPHA-PINENE
17%
OTHER MONOTERP.
15%
ISOPRENE
24%
OTHER MONOTERP.
17%
ISOPRENE
17%
UNKNOWN HC
46%
UNKNOWN HC
49%
Figure 4-3. Distribution of Biogenic Hydrocarbon Components for Each Season for the United States.
-------
SEASON-WINTER
SEASON-SPRING
ALPHA-PINENE
36X
ALPHA-PINENE
32X
OTHER MONOTERP.
31X
OTHER MONOTERP.
31X
UNKNOWN HC
33X
ISOPRENE
2X
UNKNOWN HC
35X
OTHER MONOTERP.
25X
SEASON-SUMMER
ALPHA-PINENE
25X
SEASON-AUTUMN
ISOPRENE
10X
OTHER MONOTERP.
2BX
ALPHA-PINENE
29X
ISOPRENE
6X
UNKNOWN HC
40X
UNKNOWN HC
37X
Figure 4-4. Distribution of Biogenic Hydrocarbon Components for Each Season for Canada.
-------
hydrocarbons. Mean maximum and minimum monthly temperatures for state climatic
divisions from the Geoecology Data Base were used to generate diurnal profiles and solar
radiation was calculated seasonally for each climatic division. In addition, a canopy model
was developed for calculation of emissions within the forest canopy.
In the third generation inventory2, geometric mean emission rates were calculated from
Zimmerman's2^ data and corrected to ambient conditions using Tingey's relationships.22 Mean
monthly maximum and minimum temperature by state climatic division were used for this
inventory.
Data from the three generations of biogenic hydrocarbon emissions inventories dp.vp.lnpft.d-hy
j^amb, & nl1-2 yielded tnta^ annnal_biogenic hydrocarbon emissions of 30.7, 19. and 27 Tg,
respectively. Total hydrocarbon emissions calculated forjhis project are 18.8 Tg annually.
comparison of seasonal totals from the first and third generation inventories with those frorn
the current project is presented in Table 4-3. The data in this table indicate that in all cases,
seasonal data reported by Lamb, et al are higher than those determined in the current effort.
On a percentage basis however, the relative seasonal contributions from the first generation
inventory are very similar to those developed for this project. The relative seasonal
contributions for the third generation inventory are also similar with the exception of the
spring and fall. In the first generation and current inventory, the contribution to total annual
hydrocarbons is greater in the fall than the spring. In the third generation inventory, the
reverse is true.
Compound specific data are presented in Table 4-4 for the first and third generation
inventories and for the current effort. The data in this table indicate fewer similarities among
the inventories on a compound specific basis. In all cases, the contribution by unknown or
"other" hydrocarbons is the greatest.
Differences in the magnitudes of emissions are most likely due to several factors including
differences in emission factors, input climatological data, and growth and biomass factors. A
detailed sensitivity study would be required to determine the predominant factors influencing
these differences. A qualitative comparison of data used in each of these studies indicates
that biogenic hydrocarbon emissions are very sensitive to biomass growth assumptions and
climatological data. Additionally, as noted by Lamb, et al1;2 the uncertainty in these estimates
due to the emissions algorithms, emissions rate measurements, biomass densitiesjand land use
areas is approximately a factor of 3.
Gridded emissions of isoprene, monoterpenes, alpha-pinene, unknown hydrocarbons, and total
hydrocarbons for the summer are presented graphically in Figures 4-5 through 4-9.
Corresponding graphical representations of gridded annual total biogenic hydrocarbons and
grassland NO, are presented in Figures 4-10 and 4-11, respectively. The resolution of the
data presented in the seasonal maps (Figures 4-5 through 4-9) are expressed in kilograms per
35
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TABLE 4-3. SEASONAL TOTALS FOR BIOGENIC EMISSIONS INVENTORIES
Winter
Spring
Summer
Fall
TOTAL
1st Generation
Tg
1.50
6.00
15.90
7.30
30.70
Inventory1
%
4.9
19.5
51.8
23.8
100.0
3rd Generation
Tg
0.97
5.89
16.15
4.35
27.36
Inventory2
%
3.5
21.5
59.0
16.0
100.0
Current
Tg
0.74
3.90
10.04
4.12
18.80
Effort
%
3.9
20.8
53.4
21.9
100.0
TABLE 4-4. COMPOUND SPECIFIC ANNUAL TOTALS FOR BIOGENIC EMISSIONS
INVENTORIES
1st Generation Inventory1 3rd Generation Inventory2
Tg %
isoprene 5.10 16.6
alpha-pinene 6.60 21.5
other monoterpenes --'
unknown hydrocarbons 19.0 61.9
TOTAL 30.7 100.0
Tg
7.45
4.36
6.23
9.32
27.36
%
27.2
15.9
22.8
34.1
100.0
Current Effort
Tg
3.79
3.11
3.12
8.78
18.80
%
20.2
16.5
16.6
46.7
100.0
'Lamb, et al, 1987
2Lamb, et al, 1990
3Other monoterpenes are not reported in this inventory
36
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hectare (kg/ha) for the 91 days representing the summer season, and the annual maps (Figures
4-10 and 4-11) of total hydrocarbon and NOX are totals for all four seasons combined. The
grid system in these plots have approximate 80 x 80 km grid cell dimensions.
Gridded isoprene emissions (Figure 4-5) for the summer indicate a large area of maximum
isoprene in the southeastern U.S. Smaller areas of maxima are also found in California,
Arizona, and Texas. These maxima correspond to areas of maximum coverage of oak and
deciduous species reported in the Geoecology Data Base. Additionally, the southern areas of
the U.S. report the warmest temperatures and receive the maximum solar insolation,
especially during the summer. Minimum isoprene emissions were calculated for Alberta,
Saskatchewan, Minnesota, and Iowa.
Gridded summer emissions of alpha-pinene and other monoterpenes (Figures 4-7 and 4-6,
respectively) exhibit almost identical patterns for the summer. Maximum emissions of alpha-
pinene and other monoterpenes are located in much of the Pacific Northwest (British
Columbia, southwestern Alberta, Washington, Idaho, western Montana, Oregon, and northern
California) and portions of Ontario, Quebec, Oklahoma, Texas, Louisiana, and Georgia.
These calculated maxima correspond to large areas of coniferous canopy coverage reported in
the Geoecology Data Base. Minimum values for alpha-pinene and other monoterpenes are
found in eastern Alberta and Saskatchewan, coinciding with areas of minimal coniferous
coverage.
Maximum unknown hydrocarbon emissions for the summer (Figure 4-8) are found in much of
the southeastern and midwestern States. Emissions of unknown hydrocarbons may be
influenced by emissions from crops in these agricultural areas. This may be particularly true
for corn due to its relatively large emission factor. Minima for unknown hydrocarbons are
located in Eastern Alberta and Saskatchewan. These minima correspond to areas of sparse
canopy coverage for all forest types.
Figure 4-9 exhibits gridded biogenic emissions of total hydrocarbons for the summer. Areas
of maximum emissions correspond well with areas of high isoprene (Figure 4-5) and
unknown hydrocarbon (Figure 4-8) emissions. A comparison of Figure 4-9 with the county
averaged hydrocarbon flux (kg/ha) for the summer reported by Lamb, et al1 indicates similar
patterns of maxima; however, the magnitudes reported by Lamb, et al are higher by a factor
of about 1.5. Additionally, areas of maximum total hydrocarbon emissions in Iowa and
Illinois, shown in Figure 4-9, do not coincide with emissions in these states reported by
Lamb, et al.
Annual gridded total biogenic hydrocarbon emissions (Figure 4-10) exhibit similar patterns of
maxima and minima as summer isoprene and total hydrocarbon emissions (Figures 4-5 and 4-
9, respectively). This also corresponds to the maximum contribution of summertime biogenic
hydrocarbon emissions to total hydrocarbons, presented in Figure 4-1. A maximum
contribution of summertime hydrocarbon emissions to total hydrocarbons results from a
maximum vegetation growth, warmer temperatures, and maximum solar insulation.
37
-------
Figure 4-5 Seasonal Gridded Biogenic Emissions of Isoprene
for Summer
U)
oo
KG/HECTARE
0 - 0.2
0.2 - 0.5
0.5 - 1.5
1.5 - B
>8
-------
Figure 46 Seasonal Gridded Biogenic Emissfons of Other Monoterpenes
for Summer
OJ
KG/HECTARE
] 0 - 0.3
0.3 - 1.0
1.0 - 2
2 - 3.5
>3.5
-------
Figure 47 Seasonal Gridded Biogenic Emissions of Alphapinene
for Summer
KG/HECTARE I I 0 - 0.3
0.3 - 1.0
1.0-2
2 - 3.5
>3.5
-------
Figure 4 8 Seasonal Grldded Biogenfc tmissions of Unknown HC
for Summer
KG/HECTARE
0-0.5
0.5-2
2-5
5-10
-------
Figure 49 Seasonal Gridded Biogenic Emissions of Total Hydrocarbons
for Summer
to
KG/HECTARE
0-2
2-7
7-14
14 - 20
>20
-------
Figure 410 Annual Gridded Biogenic Emissions of Total Hydrocarbons
KG/HECTARE
I 0-2
2-10
10 - 20
20 - 40
>40
-------
Annual grassland NOx emissions (Figure 4-11) show maxima in the plain States and eastern
Texas. These maxima coincide well with grassland areas reported in the Geoecology Data
Base.
Seasonal and State/Province Emissions Data
Seasonal state-level totals calculated for the winter months (Tables A-6 and A-10) indicate
that States and provinces with the larger land areas generally contain higher State level total
biogenic hydrocarbon emissions. In order of decreasing emissions, the States/provinces with
the highest biogenic hydrocarbon emissions totals for the winter are: British Columbia
(113,211 tons/year), Quebec (108,685 tons/year), Florida (106,263 tons/year), California
(80,878 tons/year), and Ontario (74,938 tons/year). Biogenic hydrocarbon emissions from
these five States/provinces account for 40% of the total hydrocarbon emissions for the winter
season.
Contributions from each of the individual hydrocarbon species to maximum total hydrocarbon
emissions for each of the five States or provinces noted above was evaluated. For British
Columbia, Quebec, California, and Ontario, alpha-pinene, other monoterpenes, and unknown
hydrocarbons each contribute approximately one third to the total hydrocarbon in winter. In
California, isoprene contributes <0.1% while in Florida the contribution of isoprene is about
16%. The Canadian provinces report zero isoprene emissions for the winter. In Florida
alpha-pinene and other monoterpenes each contribute just under 20% and the contribution
from unknown hydrocarbons is about 50% of the total winter hydrocarbon emissions.
Locations of maxima for each hydrocarbon species during the winter are: Florida for isoprene
(71.2% of total isoprene); British Columbia for monoterpenes (9% of total monoterpenes);
Quebec for alpha-pinene (11% of total alpha-pinene); Florida for unknown hydrocarbons
(12% of total unknown hydrocarbons); and Texas for NOX (41% of total grassland NOJ. In
the U.S., maximum State-level monoterpene and alpha-pinene emissions during the winter are
found in California.
Seasonal State-level totals calculated for the spring (Tables A-7 and A-ll) indicate that
geographic location (e.g., latitude) and total land area are determining factors for States and
provinces with maximum biogenic hydrocarbon emissions. In order of decreasing emissions.
the States/provinces with the highest biogenic hydrocarbon emissions totals for the spring are:
Quebec (365,668 tons/year), Texas (328,309 tons/year), Ontario (305,041 tons/year). Georgia
(257,274 tons/year), British Columbia (255,214 tons/year), and Florida (252,376 tons/year).
Biogenic hydrocarbon emissions from these six States/provinces account for 32% of the total
hydrocarbon emissions for the spring season.
Contributions from each of the four hydrocarbon species analyzed to maximum total
hydrocarbon emissions in the spring for each of the six States or provinces noted above were
44
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Figure -41 1 Annual Gridded Biogenic Emissions of Grassland NOx
KG/HECTARE
0 - 0.004
0.20 - 0.60
0.004 -0.01
>O.BO
0.01 - 0.20
-------
evaluated. Quebec, Ontario, and British Columbia show similar contributions, of about one
third, for alpha-pinene, other monoterpenes, and unknown hydrocarbons. For Quebec and
Ontario, there is also a 3% contribution from isoprene. In Texas, Georgia, and Florida, the
maximum contribution to total hydrocarbon is from unknown hydrocarbons (43, 50, and 46
percent, respectively). The contributions from alpha-pinene and other monoterpenes are
almost equal within each of the three States: alpha-pinene = 18% and monoterpenes = 17%
for Texas; alpha-pinene = 10% and monoterpenes = 10% for Georgia; alpha-pinene = 13%
and monoterpenes = 13% for Florida.
Locations of maxima by individual hydrocarbon species during the spring are: Georgia for
isoprene (10% of total isoprene); Quebec for alpha-pinene and other monoterpenes (10% of
total alpha-pinene and 10% of total other monoterpenes); Texas for unknown hydrocarbons
(6% of total unknown hydrocarbons); and Texas for grassland NOX (21% of total grassland
NOX). For the U.S., maximum State-level monoterpenes and alpha-pinene are each found in
Texas.
Isoprene values for Canada (Table A-ll) for the spring indicate zero values for the western
provinces (Manitoba, Saskatchewan, Alberta, and British Columbia). A review of the
monthly canopy biomass factors indicated that during the spring there is no growth for oak
and deciduous species above row 145 (this corresponds to the U.S.-Canadian border west of
Ontario). Therefore, eastern provinces with land area below row 145 may exhibit some
isoprene during the spring months, while provinces in western Canada have zero emissions
for isoprene.
During the summer months, calculated seasonal State level totals (Tables A-8 and A-12)
indicate that total land area is an important determinant for maximum State level biogenic
hydrocarbon emissions. In order of decreasing emissions, the States/provinces with the
highest biogenic hydrocarbon emissions totals for the summer are: Quebec (1,232,164.8
tons/year), Ontario (1,062,365 tons/year), Texas (664,763 tons/year), British Columbia
(637,676 tons/year), and California (570,154 tons/year). Biogenic hydrocarbon emissions
from these five States/provinces account for 27% of the total hydrocarbon emissions for the
summer.
Contributions from each of the four hydrocarbon species analyzed to the above
States/provinces varies for the U.S., but shows similar patterns for the three Canadian
provinces. In Quebec, Ontario, and British Columbia, about 10% of the total hydrocarbon
emissions are isoprene, about 25% each are monoterpenes and alpha-pinene, and the
remaining 40% are unknown hydrocarbons. In California, the breakdown of hydrocarbons is
22% isoprene, 20% monoterpene, 20% alpha-pinene, and 38% unknown hydrocarbons. In
Texas. 31% of total hydrocarbons are isoprene, 15% alpha-pinene, 16% other monoterpenes,
and 38% an unknown hydrocarbons.
Locations of maxima for individual species for the summer are: Texas for isoprene (7% of
total isoprene), Quebec for monoterpenes (11% of total monoterpenes), alpha-pinene (11.5%
46
-------
of total alpha-pinene), and unknown hydrocarbons (7% of total unknown hydrocarbons), and
Texas for grassland NOX (13% of total grassland NOJ. For the U.S., maximum State level
monoterpene and alpha-pinene emissions occur in California and maximum unknown
hydrocarbon emissions occur in Illinois.
Seasonal State level totals for the autumn, (Tables A-9 and A-13), indicate that geographic
location (e.g., latitude) and total land area are important determinants for maximum
State/province level total biogenic hydrocarbon emissions. In order of decreasing emissions,
the States/provinces with the highest total biogenic hydrocarbon emissions are:
Quebec (474,278 tons/year), Ontario (373,239 tons/year), Texas (321,371 tons/year), Florida
(271,348 tons/year), and Georgia (265,150 tons/year). Biogenic hydrocarbon emissions from
these five States/provinces account for 28% of the total hydrocarbon emissions for autumn.
Contributions from each of the individual hydrocarbon species to maximum total hydrocarbon
emissions for the States/provinces discussed in the previous paragraph indicate similar species
distributions among the two Canadian provinces and for the three States. Individual species
contributions to total biogenic hydrocarbons however indicate marked differences between the
U.S. and Canada for the five maxima. In Quebec and Ontario, a little more than one quarter
of the biogenic hydrocarbon emissions are each alpha-pinene and other monoterpenes, about
5% are isoprene, and the remaining 35-40% are unknown hydrocarbons. In Florida and
Georgia, about 25% of the biogenic hydrocarbon emissions are isoprene, about 10-15% each
are alpha-pinene and other monoterpenes, and approximately 50% are unknown hydrocarbons.
In Texas, 20% of the hydrocarbon emissions are comprised of isoprene, alpha-pinene and
other monoterpenes each contribute just under 20%, and about 45% are unknown
hydrocarbons.
Locations of maxima for individual species for the autumn are: Alabama for isoprene (8.5%
of total isoprene), Quebec for monoterpenes, alpha-pinene, and unknown hydrocarbons
(11.3% of total monoterpenes; 11.6% of total alpha-pinene; and 6% of total unknown
hydrocarbons), and Texas for grassland NOX (19.4% of total grassland NOJ. In the U.S.,
maximum State-level monoterpenes occur in California and Texas. Alpha-pinene and
unknown hydrocarbon maxima occur in Georgia and Texas.
47
-------
SECTION 5
SUMMARY AND RECOMMENDATIONS
SUMMARY
The objective of the work documented in this report is to develop county and State-level
emissions inventories of biogenic hydrocarbon and NOX emissions representative of the
monthly, seasonal and annual temporal scales. The intended application of these inventories
is primarily to support assessment activities. The methodology followed to achieve this
objective was to calculate gridded hourly biogenic emissions for a representative day in each
month and to sum these emissions to larger temporal and geographic scales. This
methodology is closely related to similar work that has been performed by EPA's
Atmospheric Research and Exposure Assessment Laboratory (EPA AREAL) to develop
gridded emissions inventories of biogenic hydrocarbons for specific episodic cases in support
of RADM and ROM model evaluation studies.
Biogenic hydrocarbon emissions have been shown to be strongly influenced by environmental
factors such as ambient temperature and solar radiation. To address these dependencies, a
Canopy Model was developed by researchers at Washington State University. As part of this
current project, diurnal profiles of temperature, solar radiation, wind speed, and relative
humidity were developed for use with the Canopy Model. The methodology and detailed
processes for developing these data on the appropriate temporal and spatial scales is
documented in Section 2 of this report.
Biogenic hydrocarbon emissions were calculated for each hour of a typical day for each
month using gridded leaf biomass and land use data, biomass and growth factors, species
specific emissions factors, and the Canopy model. A summary of this methodology and the
procedures used to temporally and spatially aggregate the data can be found in Section 3 of
this report. Use of representative monthly diunal profiles of meteorological data represent a
refinement over the monthly and seasonal average meteorological data used in previous
studies.
A summary of the resultant emissions data at the seasonal and annual levels is provided in
Section 4. The data summaries indicate that biogenic hydrocarbon emissions and NOX are
highest during the summer and lowest during the winter. For the spring and fall, the
magnitudes of the biogenic emissions are similar for the U.S. In Canada, biogenic
hydrocarbon emissions are higher in the fall than in the spring. Analysis of State-level
seasonal totals indicates that during the winter and summer, total State land area appears to be
the controlling factor in determining States and provinces with the highest biogenic
hydrocarbon emissions magnitudes, although, emissions are also dependent on the canopy
foliage biomass. In the spring and fall, land area as well as geographic location appear to be
important.
48
-------
RECOMMENDATIONS
Recommendations for future efforts include additional quality control checks and analyses to
evaluate the characteristics and distributions of biogenic hydrocarbon emissions at the
monthly, seasonal and annual temporal scales and at the grid, county, and State levels.
Specifically, the relationship between forest and other biomass data that is used in the
calculations should be reviewed carefully and compared with the resulting emissions
estimates. An evaluation of canopy biomass growth factors for the spring should also be
undertaken to determine validity of zero isoprene emissions calculated for the western
provinces during the spring.
Sensitivity studies to determine the effects of the various biogenic inputs on the emissions
calculations should also be undertaken. For example, comparison of data generated from a
previous project with the results of this effort have indicated the impacts of monthly canopy
biomass factors on biogenic hydrocarbon emissions. This study would allow a more detailed
analysis of differences in the current and previous inventories noted in Section 4.
Further research should be conducted to determine if this or a similar methodology could be
applied to develop emissions inventories of biogenic hydrocarbons and soil NOX at global
scales in support of global change studies. Additionally, NOX emissions in the biogenic
inventory should be expanded to include other natural sources such as other land use areas,
biomass burning, and lightning.
Other possible improvements to the biogenic hydrocarbon and NOx emissions estimation
methodology include: the use of updated land use data; investigation of improved growth
factors and physiological relationships; identification of unknown hydrocarbon compounds;
and expanding the soil NOx emissions algorithms to include other land use types (e.g., forest
land and fertilized crop land).
49
-------
SECTION 6
REFERENCES
1. Lamb, B., A. Guenther, D. Gay, and H. Westberg. A National Inventory of Biogenic
Hydrocarbon Emissions. Atmospheric Environment, Vol. 21 No. 8 pp. 1695-1705.
1987.
2. Lamb, B., D. Gay, H. Westberg, and E. Allwine. Development of a National
Inventory for Natural Hydrocarbon Emissions. Presented at the NAPAP 1990
International Conference on Acidic Deposition: State of Science and Technology,
February 11-16, 1990. Hilton Head Island, SC.
3. Chameides, W.L., R.W. Lindsay, J. Richardson, C.S. Kiang. The Role of Biogenic
Hydrocarbons in Urban Photochemical Smog: Atlanta as a Case Study. Science, Vol.
241 pp. 1473-1475. 1988.
4. Trainer, M. et al. Models and Observations of the Impact of Natural Hydrocarbons on
Rural Ozone. Nature, Vol. 329, pp. 705-707. 1987.
5. Modica, L.G., D.R. Dulleba, R.A. Walters, and I.E. Langstaff. Flexible Regional
Emissions Data System (FREDS) Documentation for the 1985 NAPAP Emissions
Inventory. EPA-600/9-89-047 (NTIS PB89-198816). U.S. Environmental Protection
Agency, Research Triangle Park, NC. May 1989.
6. Saeger, M. et al. The 1985 NAPAP Emissions Inventory (Version 2): Development
of the Annual Data and Modelers' Tapes. EPA-600/7-89-012a(NTIS PB91-119669).
U.S. Environmental Protection Agency, Research Triangle Park, NC. November 1989.
7. U.S. Department of Commerce. Surface Airways Hourly TD-3280. Prepared by
National Oceanic and Atmospheric Administration, National Environmental Satellite
and Data Information Service, National Climatic Data Center, Asheville, NC. March
1986.
8. U.S. Department of Commerce. U.S. Airways Surface Weather Observations TDF
1440 Format. National Oceanic and Atmospheric Administration, National
Environmental Satellite and Data Information Service, National Climatic Data Center,
Asheville, NC. October 1975.
9. U.S. Department of Commerce. Daily Weather Maps Weekly Series. National
Oceanic and Atmospheric Administration, National Weather Service, National
Meteorological Center, Climate Analysis Center. Washington, DC. June 1984.
50
-------
10. Rand McNally & Company. 1988 Rand McNally Road Atlas. United States/
Canada/Mexico. 128 pp. 1988.
11. The SAS Institute, Inc. SAS User's Guide: Basics, Version 5 Edition. The SAS
Institute, Inc., Gary, NC.
12. Barnes, S., 1964: A Technique for Maximizing Details in Numerical Weather Map
Analysis. Journal of Applied Meteorology, 3, pp. 396-409.
13. Barnes, S., 1973: Mesoscale Objective Map Analysis Using Weighted Time-Series
Observations. NOAA Technical Memorandum, ERL NSSL-62. 38 pp.
14. Bullock, O., 1988: Objective 2-D Spatial Analysis Subroutines for ROM Processor
Applications (User's Guide). Atmospheric Science Research Laboratory, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC.
15. Causley, M.C., J. L. Fieber, M. Jimenez, and L. Gardner. User's Guide for the Urban
Airshed Model Volume IV: The Emissions Preprocessor System. EPA-450/4-90-007d
(NTIS PB-91-131250). U.S. Environmental Protection Agency, Research Triangle
Park, NC. June 1990.
16. Kasten, F. and G. Czeplak. Solar and Terrestrial Radiation Dependent on the Amount
and Type of Cloud. Solar Energy, 24, pp. 177-189. 1980.
17. Olson, R.J. Geoecology: a County-Level Environmental Data Base for the
Conterminous United States. Publication Number 1537, Oak Ridge National
Laboratory, Environmental Sciences Division, Oak Ridge, TN. 1980.
18. Page, S.H. National Land Use and Land Cover Inventory. Lockheed Engineering and
Management Services Company, Inc. Remote Sensing Laboratory, Las Vegas, NV.
Prepared for Office of Research and Deyelopment, U.S. Environmental Protection
Agency, Las Vegas, NV. 7 pp. April 1980.
19. Matthews, E. Vegetation, Land Use and Seasonal Albedo Data Sets: Documentation
of Archived Data Tape. NASA Technical Memorandum 86107, National Aeronautics
and Space Administration, Goddard Space Flight Center, Institute for Space Studies.
New York, NY. May 1984.
20. Toler, E.T. Personal Communication on "Quality Control of Biomass Data for
NAPAP Biogenic Hydrocarbon Emissions Estimates." September 1989.
21. U.S. Department of Agriculture, Agricultural Statistics 1988. U.S. Government
Printing Office, Washington, DC. 1988.
51
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22. Tingey, D.T. The Effect of Environmental Factors on the Emissions of Biogenic
Hydrocarbons from Live Oak and Slash Pine. In: J.J. Bufalini and R.R. Arnts (eds.)
Atmospheric Biogenic Hydrocarbons, Vol. 1, Emissions. Ann Arbor Science, Ann
Arbor, MI. 1981.
23. Barnard, W.R., Development of County-level Wind Erosion and Unpaved Road
Alkaline Emission Estimates for the 1985 NAPAP Emissions Inventory. EPA-600/7-
90-005 (NTIS PB90-172 586). U.S. Environmental Protection Agency, Research
Triangle Park, NC. January 1990.
24. Zimmerman, P.R. Tampa Bay Area Photochemical Oxidant Study, Appendix C:
Determination of Emission Rates of Hydrocarbons from Indigenous Species of
Vegetation in the Tampa/St. Petersburg Area. EPA-904/9-77-028 (NTIS PB297057).
U.S. Environmental Protection Agency, Region 4, Atlanta, GA. February 1979.
52
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APPENDIX A
BIOGENIC EMISSIONS DATA SUMMARIES
A-l
-------
KEY TO APPENDIX A TABLES
Column Headings
Description
TSP
CA1
CA2
CAS
Kl
K2
K3
MG1
MG2
MG3
NA1
NA2
NA3
PM1
PM2
PM3
isoprene
alpha-pinene
unknown hydrocarbons
NO
NO2
total hydrocarbon
total NO,
total suspended paniculate
calcium, 0.0 2.5 micrometer
calcium, 2.5 - 10.0 micrometer
calcium, > 10 micrometers
potassium, 0.0 2.5 micrometer
potassium, 2.5-10 micrometer
potassium, > 10 micrometer
magnesium, 0.0 - 2.5 micrometer
magnesium, 2.5 - 10 micrometer
magnesium, > 10 micrometer
sodium, 0.0 2.5 micrometer
sodium, 2.5-10 micrometer
sodium, > 10 micrometer
total paniculate, 0.0 2.5 micrometer
total paniculate, 2.5 6.0 micrometer
total paniculate, 6.0 10.0 micrometer
isoprene, 2-methyl-1,3-butadiene
CH2:CHC(CH,):CH2
Ci0H16
carbon-containing compounds of
unknown structure
nitric oxide, represented as NO
nitrogen dioxide, represented as NO2
the sum of all hydrocarbon
the sum of grassland NO and NO,
(nitrogen oxides)
All emissions data represented in the Tables of Appendix A are represented in short tons,
(2000 pounds). One metric ton equals 1.10231707 short tons.
A-2
-------
TABLE A-1 1985 NAPAP Modelers' Emission Inventory Version 2 (Revised) - U.S. Natural Source Paniculate Emissions by State (Tons/Year)
(continued)
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Lousiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
TSP
572,553
1 ,900,489
490.365
2,566.856
489,823
64,097
11,210
2
400.908
1,036,711
986.397
796.581
2.094,995
907.590
1 .281 .054
266,432
347.433
377,463
77.153
41 1 ,090
1 .368,943
1 .350.252
970,608
1 ,704.624
2,272,633
698,367
2,227,309
42.730
32,798
1 .657.678
1 .484.294
171.348
722,386
2,361,063
1 ,563,998
2,217,862
912,482
64,986
133,611
357,975
449,458
8.969,735
2,135,604
166,246
416,693
498,903
221,394
358,524
788,664
51 ,400,375
CA1
686
740
587
1,550
101
77
13
0
478
1,007
870
1,461
4,001
1,577
1.325
317
411
452
92
488
1,478
1,531
1.162
1,902
713
757
1.148
51
37
931
673
27
662
1,893
1,197
286
1,331
74
20
353
680
8,798
1,494
199
253
571
257
536
150
45,400
CA2
7,153
7,714
6,119
16,164
1,054
801
138
0
4,985
10,506
9,078
15,233
41,720
16,451
13,819
3,311
4,289
4,716
956
5.092
15,413
15,963
12.121
19,839
7,438
7,895
1 1 ,973
534
386
9,714
7,014
286
6,908
19.739
12,486
2,984
13,875
774
212
3,678
7,092
91,745
15,584
2,075
2,638
5,957
2,684
5,589
1,561
473,454
CAS
32,664
35,222
27,940
73,809
4,812
3,657
631
0
22,761
47,975
41,450
69,556
190,501
75,117
63,100
15,117
19,584
21 ,534
4,363
23,252
70,381
72,889
55,349
90,589
33,964
36.049
54,672
2.438
1.764
44,355
32,027
1,305
31,543
90,134
57,015
13.625
63,358
3,536
966
16,795
32,383
418,929
71,161
9,474
12,045
27,200
12,255
25,518
7,127
2,161.889
K1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MG1
129
194
110
371
33
14
2
0
89
192
177
142
469
182
263
60
77
85
17
92
283
278
218
380
345
146
276
10
7
229
333
30
139
509
322
395
100
14
4
73
101
1.529
302
37
93
109
48
77
95
9.179
-------
TABLE A-1 1985 NAPAP Modelers' Emission Inventory Version 2 (Revised) - U.S. Natural Source Paniculate Emissions by State (Tons/Year)
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Lousiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
MG2
1.342
2,018
1,148
3.872
347
150
26
0
930
1,999
1,848
1,476
4,888
1,893
2,747
622
806
885
179
958
2.951
2,900
2,274
3.961
3,594
1.525
2.874
100
73
2,392
3,468
316
1,450
5,307
3,353
4,116
1,039
147
45
760
1,053
15.942
3,151
389
967
1,138
505
808
996
95.727
MG3
6,126
9,214
5,241
17,679
1,583
686
119
0
4,248
9,128
8,437
6,739
22,322
8,643
12,541
2,840
3,679
4.039
820
4,375
13,476
13,241
10,382
18,085
16,413
6,965
13,124
457
334
10,924
15,836
1,443
6,619
24,231
15,312
18,796
4,744
671
204
3,469
4,809
72,795
14,388
1,778
4,415
5,195
2,308
3,688
4.547
437,108
NA1
0
0
. 0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
NA2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
o
NA3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
o
PM1
12,024
39,910
10,298
53,904
10,286
1,346
235
0
8,419
21.771
20,714
16,728
43,995
19,059
26,902
5,595
7,296
7,927
1,620
8,633
28,748
28,355
20,383
35,797
47,725
14,666
46,773
897
689
34,811
31,170
3,598
15,170
49,582
32,844
46,575
19,162
1,365
2,806
7,517
9,439
188,364
44,848
3,491
8,751
10,477
4,649
7,529
16.562
1 .O79.4O8
PM2
50,957
169,144
43,642
228,450
43,594
5,705
998
0
35,681
92,267
87,789
70,896
186,455
80,776
114,014
23,712
30,922
33,594
6,867
36,587
121,836
120,172
86,384
151,712
202,264
62,155
198,231
3,803
2,919
147,533
132.102
15,250
64,292
210,135
139,196
197,390
81,211
5,784
11,891
31.860
40,002
798,306
190,069
14,796
37,086
44,402
19,704
31,909
70,191
4,574.633
PM3
74,432
247,064
63,747
333,691
63,677
8,333
1,457
0
52,118
134,772
128,232
103,555
272,349
117,987
166,537
34,636
45,166
49,070
10,030
53,442
177,963
175,533
126,179
221,601
295,442
90,788
289,550
5,555
4,264
215,498
192,958
22,275
93,910
306,938
203,320
288,322
118,623
8,448
17,369
46,537
58,430
1,166,066
277,629
21,612
54,170
64,857
28,781
46,608
102,526
6.682.O49
-------
TABLE A-2 1985 NAPAP Modelers' Emissions Inventory Version 2 (Revised) - U.S. Natural Source Particulate Emissions by EPA Region (Tons/Year)
REGION
1
It
III
IV
V
VI
VII
VIM
IX
X
TOTAL
TSP
1,126,614
1.517,092
1,638,933
4,001,630
8,330,358
13.029,209
4.591,636
6.767,086
6.694,654
3.703,163
51.400,375
CA1
1,342
710
1,946
4.379
10,899
1 1 .924
5,562
3,473
3.438
1.728
45.400
CA2
13,992
7,400
20.291
45,666
113,657
124.353
58,003
36,223
35,851
18.018
473,454
CA3
63,890
33,791
92,652
208,520
518,980
567,822
264,854
165,402
163.703
82,275
2.161.889
K1
0
0
0
0
0
0
0
0
0
0
0
K2
0
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
0
0
0
0
0
0
MG1
252
340
261
823
1,758
2,267
971
987
840
681
9,179
REGION
I
II
III
IV
V
VI
VII
VIM
IX
X
TOTAL
MG2
2.629
3,541
2.717
8.580
18.330
23,641
10,125
10.297
8,764
7,102
95.727
MG3
12,006
16,170
12,406
39,180
83,696
107.951
46.234
47.018
40.017
32.429
437,107
NA1
0
0
0
0
0
0
0
0
0
0
0
NA2
0
0
0
0
0
0
0
0
0
0
0
NA3
0
0
0
0
0
0
0
0
0
0
0
PM1
23,659
31 ,859
34,418
84,034
174,938
273,613
96,424
142,109
140,588
77,766
1 ,079.408
PM2
100,269
135,021
145,865
356.145
741 .402
1,159,600
408,656
602,271
595,824
329,581
4,574,633
PM3
146.460
197.222
213.061
520,212
1,082,947
1.693,797
596,913
879,721
870,305
481 ,41 1
6,682.049
-------
TABLE A-3 1985 NAPAP Modelers' Emissions Inventory Version 2 (Revised) - U.S. Natural Source Participate Emissions by Source Category (Tons/Year)
sec
901
902
903
TOTAL
TSP
36,922,642
4,711,540
9,766,192
51,400,375
CA1
40,790
2,561
2,048
45,400
CA2
425,381
26,710
21,363
473,454
CA3
1,942,380
121,963
97,547
2,161,889
K1
0
0
0
0
K2
0
0
0
0
K3
0
0
0
0
MG1
7,884
692
603
9,179
sec
901
902
903
TOTAL
MG2
82,224
7,219
6,284
95.727
MG3
375,452
32,963
28,693
437,107
NA1
0
0
0
0
NA2
0
0
0
0
NA3
0
0
0
0
PM1
775,375
98,942
205,090
1,079,408
PM2
3,286,115
419,327
869,191
4,574,633
PM3
4,799,944
612,500
1,269,605
6,682,049
-------
TABLE A-4 1985 NAPAP Modelers' Emissions Inventory Version 2 - Canadian Natural Source Paniculate Emissions by Province (Tons/Year)
PROVINCE
Newfoundland
Prince Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
TSP
224.490
44,455
683,354
406,335
2,288,654
5,114,660
3,126,754
7,093,746
8,586,539
1,959,164
29,528.152
CA1
71
2
31
71
539
1,755
854
1,301
1,294
524
6,442
CA2
102
4
45
103
776
2,618
1,269
2,336
2,385
767
10,404
CAS
238
45
135
273
2,412
9,869
4,261
18,430
20,237
2,320
58,218
K1
0
0
0
0
0
0
0
0
0
0
0
K2
0
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
0
0
0
0
0
0
MG1
20
1
10
22
149
503
250
462
510
157
2,083
PROVINCE
Newfoundland
Prince Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
MG2
29
2
14
31
222
789
384
920
1.021
233
3,645
MG3
68
24
49
91
827
3,861
1,608
9,245
10,402
838
27,014
NA1
0
0
0
0
0
0
0
0
0
0
0
NA2
0
0
0
0
0
0
0
0
0
0
0
NA3
0
0
0
0
0
0
0
0
0
0
0
PM1
66,205
1 1 ,492
201 ,493
118,517
650.840
1,352,988
878,744
1,561,825
1 ,933,040
555,123
7,330,267
PM2
61,251
10,263
189,125
109,661
600,544
1.255.165
842,892
1 ,504,770
1,855,012
512,960
6,941.643
PM3
29,843
5,201
91 ,992
53,575
295,359
628,505
413,704
788,159
966,025
252,468
3,524.831
-------
TABLE A-5 1985 NAPAP Modelers' Emissions Inventory Version 2 - Canadian Natural Source Paniculate Emissions by Source Category (Tons/Year)
>
oo
sec
41110
41120
42110
42120
42210
42220
42310
42320
43200
TOTAL
TSP
996,669
217,926
279,059
72,028
13,293,632
3.303,446
4.656,282
1,468,222
5,240,888
29,528.152
CA1
75
15
88
22
4,024
1,005
326
102
786
6.442
CA2
57
11
126
31
5.768
1,441
467
146
2,358
10,405
CA3
300
58
292
72
13,414
3,350
1,086
339
39,307
58.218
K1
0
0
0
0
0
0
0
0
0
0
K2
0
0
0
0
0
0
0
0
0
0
K3
0
0
0
0
0
0
0
0
0
0
MG1
18
4
25
6
1,154
288
120
39
430
2,083
sec
41110
41120
42110
42120
42210
42220
42310
42320
43200
TOTAL
MG2
14
3
36
9
1,654
412
172
55
1.289
3,645
MG3
71
16
83
21
3,848
959
401
128
21,488
27.014
NA1
0
0
0
0
0
0
0
0
0
0
NA2
0
0
0
0
0
0
0
0
0
0
NA3
0
0
0
0
0
0
0
0
0
0
PM1
249,167
54,481
83,718
21,608
3,988,090
991 ,034
1.396,885
440,467
104,818
7,330.267
PM2
119,600
26.151
80,927
20,888
3,855,153
957,999
1 ,350.322
425.785
104,818
6,941,643
PM3
69,767
15,255
39,068
10,084
1,861,108
462,482
651,879
205,551
209,636
3,524.831
-------
TABLE A-6. BIOGEN1C EMISSIONS ESTIMATES (TONS) FOR THE UNITED STATES BY STATE, WINTER SEASON
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District ol Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
Noith Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washngton
West Virginia
Wisconsin
Wyoming
TOTAL
Isoprone
680
44
0
79
0
0
0
0
16431
785
0
0
0
0
0
0
1573
0
0
0
0
0
703
0
0
0
0
0
0
0
0
11
0
0
0
0
0
0
98
0
0
2656
0
0
3
0
0
0
0
23.063
Monotorponos
5467
5705
5903
25911
7845
401
95
29
19540
8232
11953
573
714
111
1035
1969
10622
3132
482
530
4534
5046
4328
1949
12713
506
1002
711
443
6179
2487
5593
160
1177
7974
21028
2298
42
2875
1133
2815
11559
3310
545
2101
13494
1559
2866
6028
236.703
Alpha-Pinene
5630
5962
6208
27062
8694
441
103
31
19322
8392
13394
625
782
124
1114
2133
10929
3587
521
587
5170
5962
4469
2113
14288
560
1085
805
481
6563
2796
5854
185
1294
8425
22690
2543
46
2956
1258
2996
12168
3663
619
2260
14716
1704
3303
6764
253,377
Unknown HC
7033
6138
6314
27825
8392
429
102
31
50970
10054
12787
612
764
119
1107
2106
13500
3351
515
567
4850
5398
5723
2085
13600
541
1072
760
474
6610
2660
5998
171
1259
8530
22495
2458
45
3207
1212
3011
17221
3541
583
2253
14436
1668
3066
6449
294.094
NO
14.1
2.7
0.0
40.6
0.0
0.0
0.0
0.0
953.8
33.2
0.0
0.0
0.0
0.0
00
0.0
523.7
0.0
0.0
0.0
0.0
0.0
13.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.4
0.0
0.0
0.0
0.0
0.0
0.0
8.0
0.0
0.0
1094.5
0.0
0.0
0.1
0.0
0.0
0.0
0.0
2.686
NO2
1.3
03
0.0
3.7
0.0
0.0
00
0.0
87.7
3.1
0.0
0.0
00
00
0.0
0.0
48.2
0.0
0.0
0.0
0.0
0.0
1.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
00
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.7
0.0
0.0
100.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
247
Total HC
18810
17848
18425
80878
24931
1272
300
90
106263
27463
38134
1810
2260
354
3256
6208
36623
10070
1518
1684
14554
16407
15224
6147
40600
1608
3159
2276
1398
19352
7943
17455
517
3729
24930
66214
7300
134
9137
3602
8822
43604
10513
1746
6617
42646
4932
9236
19241
807,237
Grassland NOx
15.4
2.9
0.0
44.4
0.0
0.0
0.0
0.0
1041.5
36.3
0.0
0.0
0.0
0.0
00
0.0
571.9
0.0
0.0
0.0
0.0
0.0
15.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.5
0.0
0.0
0.0
0.0
0.0
0.0
8.7
0.0
0.0
1195.2
0.0
0.0
0.1
0.0
0.0
0.0
0.0
2933
-------
TABLE A-7. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR THE UNITED STATES BY STATE, SPRING SEASON
>
»
o
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
*lew Jersey
slow Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
"onnsylvania
Rhode Island
South Carolina
South Dakota
Tennessoo
"exas
Utah
Vermont
Viiginia
Washngton
West Virginia
Wisconsin
Wyominq
OTAL
Isoprono
73636
32784
48486
25916
632
1981
1183
243
70732
78378
0
5817
5798
1446
3273
20237
45965
8418
4994
2822
7618
5278
47195
20739
2532
1329
1140
2012
3100
6394
9813
45490
1377
6579
16065
1251
10302
272
33257
1591
21801
72934
1915
1852
25487
2178
7471
6260
574
796.547
Monoterponcs
18201
24237
19035
56333
23728
1164
568
100
33174
25074
31760
13683
8273
9958
9115
9664
27880
8999
2247
1505
14644
24416
14198
11433
35389
8435
4916
2062
1543
23515
8186
18128
2475
6508
25691
39219
7478
117
9243
6680
10677
56694
11101
1661
7914
25590
4633
12312
17133
736.69O
Alpha-Pineno
18342
24482
18981
56163
23985
1197
604
102
31797
24966
32352
14820
8868
10935
9637
9992
27065
9389
2361
1556
15255
25455
14191
11961
36479
9121
5058
2125
1596
23606
8498
18332
2693
6838
25429
39880
7761
120
9284
7075
10753
58000
11175
1723
8180
26315
4688
12791
17488
749.466
Unknown HC
102334
49988
72111
86214
28529
3869
3517
435
116673
128856
33976
91232
50308
69015
24238
46359
79035
18636
12608
5013
31569
51293
66408
48554
40519
39387
6031
4753
6000
30927
24343
85858
5616
30909
45586
43080
27204
443
48933
17187
42664
140681
13897
4170
42411
29456
15201
31277
18971
2.016.271
NO
1364
890
769
1992
1090
52
34
7
1739
1026
0
963
655
786
2845
1093
1912
62
118
59
367
626
1232
1953
1854
1818
99
14
166
2154
319
676
983
499
2448
26
279
4
500
1575
847
10030
38
28
523
86
165
246
539
47.547
NO2
126
82
71
183
100
5
3
1
160
94
0
89
60
72
262
101
176
6
11
5
34
58
113
180
171
167
9
1
15
198
29
62
90
46
225
2
26
0
46
145
78
923
4
3
48
8
15
23
50
4.374
Total HC
212513
131492
158613
224626
76874
8211
5872
880
252376
257274
98088
125551
73247
91355
46263
86252
179945
45441
22209
10896
69087
106442
141993
92687
114919
58272
17145
10953
12239
84442
50841
167808
12161
50834
112771
123430
52745
952
100716
32533
85896
328309
38087
9406
83992
83538
31993
62640
54166
4,298.974
Grassland NOx
1490
972
840
2176
1190
57
37
7
1899
1121
0
1052
715
858
3107
1194
2088
68
128
64
401
683
1345
2133
2025
1985
108
15
181
2352
349
738
1073
545
2673
29
305
5
546
1720
924
10953
42
30
571
93
180
269
588
5t.921
-------
TABLE A-8. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR THE UNITED STATES BY STATE, SUMMER SEASON
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
Now York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Viiginia
Wisconsin
Wyoming
TOTAL
soprone
195530
88440
180083
125507
33474
6651
3860
837
128562
194368
30587
21864
21202
4840
16783
66311
117808
31601
17024
9097
36798
21460
129153
83423
32953
8411
15093
10360
10267
29092
39216
130077
5944
23479
70714
31366
39906
624
84103
9198
80232
206118
32215
7590
83492
17938
26306
27182
18487
2.605.623
Monolerpcnos
31272
50325
38117
118253
63757
2900
1457
231
46354
41772
87198
37578
23316
35134
26182
20830
44517
22739
5558
3752
40119
64505
24437
27816
95805
32417
25366
5146
3870
54798
21952
34220
10129
18299
56954
91145
19193
270
15866
24587
21778
103820
35011
4357
17190
55693
9721
32979
52251
1 .700.934
Alpha-Pinono
30188
48722
35988
112029
61719
2819
1468
223
43188
39925
82397
38727
23838
36757
26122
20627
41472
22151
5556
3665
39446
63894
23302
27645
92732
33406
25456
4995
3792
53368
21691
33145
10627
18512
53105
86723
19000
261
15264
25053
20956
101305
33719
4252
17041
53841
9142
32592
50678
1.652.823
Unknown HC
180644
107024
156604
214364
109468
12825
9715
1123
165028
221838
120965
257778
150275
248436
73687
110180
128703
69182
35056
17046
127857
183873
114707
127630
135387
152303
33645
19691
17805
77924
93863
177384
24397
101788
106524
124967
95389
1180
86514
73128
97903
253520
60195
16222
107494
78741
43521
116198
73313
5.113.006
NO
2203
2223
1527
4552
6230
161
82
15
2280
1636
840
2332
1678
2222
7527
2409
2725
275
297
192
1616
2544
1928
4686
10468
7273
335
73
434
7102
1258
1386
4052
1518
5655
1280
965
10
814
6741
1808
16913
350
119
1253
1224
511
985
4018
128,725
NO2
203
205
140
419
573
15
8
1
210
151
77
215
154
204
692
222
251
25
27
18
149
234
177
431
963
669
31
7
40
653
116
127
373
140
520
118
89
1
75
620
166
1556
32
11
115
113
47
91
370
11,842
Total HC
437634
294512
410792
570154
268419
25195
16500
2415
383132
497904
321147
355946
218630
325167
142774
217947
332500
145674
63194
33560
244220
333731
291599
266514
356877
226537
99561
40193
35734
215182
176722
374825
51097
162077
287296
334200
173487
2336
201747
131966
220869
664763
161140
32421
225217
206213
88990
208951
194728
1 1 .072.386
Grassland NOx
2406
2428
1668
4971
6803
175
90
17
2490
1787
917
2547
1833
2426
8220
2631
2975
300
324
210
1765
2779
2106
5117
11430
7942
366
79
474
7755
1374
1513
4424
1658
6175
1398
1054
11
889
7361
1975
18468
383
130
1369
1336
558
1075
4387
140.567
-------
TABLE A-9. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR THE UNITED STATES BY STATE, AUTUMN SEASON
STATE
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District ol Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
>Jew Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
'onnsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
"exas
Utah
Vermont
Viiginia
Washngton
West Virginia
Wisconsin
Wyoming
OTAL
Isopreno
71706
23014
49188
26683
703
2180
1014
208
68933
70567
3336
5510
5624
1264
3205
19459
41415
7706
5037
2944
8416
4510
44600
19320
3321
1398
1317
2154
2921
" 4545
10426
42202
971
7504
17850
5726
13020
225
29351
1296
21715
65241
1135
1518
23794
2813
8804
5978
1487
763.255
Monotorponos
19783
21015
19789
58539
22119
1375
654
112
37067
27084
29901
16037
10277
12999
10438
10453
28572
10119
2655
1738
17378
23034
14950
12439
29617
10363
5536
2192
1854
20573
9497
20003
2036
8643
27015
39901
9192
134
9802
6684
11347
57034
9901
1797
8730
25083
5250
12797
16171
759.680
Alpha-Pineno
19827
21209
19659
58431
22406
1407
689
113
35219
26859
30831
17218
10935
14352
10943
10730
27678
10454
2773
1789
1809S
24480
14856
12914
31109
11305
5830
2254
1899
20689
9864
20162
2258
9081
26644
40714
9515
136
9827
7215
11359
58400
9964
1851
9000
25864
5302
13440
16731
774.284
Unknown HC
112017
41686
77784
94984
25919
5318
4094
485
130128
140641
36384
108123
64229
91571
28418
50584
80427
22051
15741
6589
46241
57715
69185
53704
35461
50731
7078
5660
7673
26505
32436
97842
4630
45238
50240
49840
40088
514
52570
19666
45818
140696
12045
4486
48775
30606
19925
38453
19090
2.250.082
NO
1534
655
860
2272
1014
74
38
7
1975
1157
178
1100
800
948
3284
1203
2037
85
147
84
645
712
1313
2178
1820
2326
38
19
207
1768
504
803
847
745
2815
342
461
5
561
1639
921
10377
25
30
624
249
247
340
822
52.837
NO2
141
60
79
209
93
7
4
1
182
106
16
101
74
87
302
111
187
8
14
8
59
65
121
200
167
214
4
2
19
163
46
74
78
69
259
31
42
0
52
151
85
955
2
3
57
23
23
31
76
4.861
Total HC
223333
106923
166420
238638
71148
10281
6451
919
271348
265150
100452
146887
91066
120185
53003
91226
178092
50330
26206
13060
90130
109738
143592
98378
99508
73798
19761
12260
14347
72312
62224
180208
9895
70466
121749
136181
71815
1009
101549
34861
90238
321371
33045
9652
90299
84366
39282
70668
53478
4,547,301
Grassland NOx
1676
716
939
2481
1108
81
42
8
2157
1263
194
1201
874
1035
3586
1313
2224
92
161
92
704
777
1433
2379
1988
2540
42
21
226
1931
550
877
925
814
3074
374
503
5
613
1790
1005
11332
28
33
682
272
270
371
897
57.698
-------
TABLE A-10. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR CANADA BY PROVINCE, WINTER SEASON
PROVINCE
Newfoundland
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
(soprano
0.0
0.0
0.0
00
00
0.0
0.0
0.0
00
0.0
Monotorpenns
11980.8
4545.7
1936.0
33158.0
22882.3
4166.9
3485.3
10333.1
35537.4
128.025.4
Alpha-Pineno
14325.9
5125.5
2240.6
40055 3
27576.5
5097.9
4215.5
11968.6
39657.2
150,263.1
Unknown HC
12816.7
4862.8
2071.1
35471.3
24478.7
4457.6
3728.5
11054.0
38016.7
136.957.4
NO
0.0
00
0.0
0.0
0.0
00
00
00
00
0.0
N02
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Total HC
39123.5
14534.0
6247.8
108684.6
74937.5
13722.3
11429.2
33355.7
113211.3
415,245.9
Grassland NOx
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
TABLE A-11. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR CANADA BY PROVINCE, SPRING SEASON
PROVINCE
Newfoundland
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
Isoprono
378.8
3527.3
21738
11328.5
8929.3
0.0
0.0
0.0
0.0
26.337.7
Monotorponos
32668.8
9302.3
5126.7
109281.9
90301.3
19033.7
14764.9
30406.1
82353.2
393.238.9
Alpha-Pinene
35667.7
9804.4
5411.6
117818.9
95555.7
20086.5
15507.5
31740.6
84761.7
416.354.5
Unknown HC
35187.0
13209.6
7444.6
127238.6
110255.2
20361.7
15795.0
32527.5
88098.8
450,117.9
NO
0.14
0.14
0.04
068
0.68
0.00
0.01
0.00
0.00
1.69
NO2
0.01
0.01
0.00
0.06
0.06
0.00
0.00
0.00
0.00
0.14
Total HC
103902.2
35843.6
20156.7
365667.9
305041 .4
59481.9
46067.4
94674.2
255213.8
1,286.049.0
Grassland NOx
0.15
0.15
0.04
0.74
0.74
0.00
0.01
0.00
0.00
1.83
-------
TABLE A-12. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR CANADA BY PROVINCE, SUMMER SEASON
PROVINCE
Newfoundland
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
Isoprene
29152.0
12348.2
7710.0
121871.0
98896.8
32071.7
27871.0
30020.5
59481.6
422.422.8
Monolerpenos
92741 2
23783 1
13658.3
312548 3
264847.4
59927.3
43477.7
70541.9
1679802
1,049.505.5
Alpha-Pinene
93000.1
23124.6
13272.3
309090.2
259101.9
58331.9
42655.4
69423.2
162619.0
1.030,618.7
Unknown HC
130411.7
42565 9
25096.9
4856553
439518.6
104908 4
81775 1
1105480
2475952
1.668.075.0
NO
1.6
0.7
0.3
4.2
3.8
4.5
1093.4
1650.1
51.6
2,810.2
N02
0.1
0.1
0.0
0.4
0.4
0.4
100.6
151.8
4.8
258.5
Total HC
345305.0
101821.9
59737.5
1232164.8
1062364.7
255239.3
195779.2
280533.5
637676.1
4,170.622.0
Grassland NOx
1.7
0.7
0.3
4.6
4.1
5.0
1194.0
1801.9
56.4
3,068.7
TABLE A-13. BIOGENIC EMISSIONS ESTIMATES (TONS) FOR CANADA BY PROVINCE, AUTUMN SEASON
PROVINCE
Newfoundland
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
TOTAL
Isoprena
6188.7
2957.8
1758.3
25629.4
19559.3
5825.9
5117.9
5777.1
11100.0
83.914.3
Monotorpones
419778
12331.6
6290.4
132876.7
100374.4
19260.3
13095.7
23713.5
686055
418.525.8
Alpha-Pinene
44973.0
12654.8
6510.1
141151 9
106162.4
20511.4
14071.3
25474.1
71912.8
443,421.6
Unknown HC
51903.7
17573.7
91903
174621 0
147142.8
27380.9
196809
31267.6
86214 2
564,974.9
NO
0.7
0.4
0.1
2.0
1.8
0.9
207.7
322.7
11.5
547.8
NO2
0.1
0.0
0.0
0.2
02
0.1
19.1
29.7
1.1
50.4
Total HC
145043.2
45517.8
23749.0
474278.9
373238.8
72978.4
51965.7
86232.3
237832.4
1.510.836.6
Grassland NOx
0.8
05
0.1
2.2
2.0
1.0
226.8
352.4
12.6
598.2
-------
APPENDIX B
REGIONAL EMISSIONS PROCESSING FOR THE
RADM: BIOGENIC SOURCES
B-l
-------
APPENDIX B CONTENTS
Figures B-3
Tables B-3
Biogenic Sources B-4
3.1 Biomass Density by Vegetation Class B-5
3.1.1 Natural Vegetation Area B-7
3.1.2 Agricultural Crop Area B-8
3.1.3 Urban Area B-8
3.1.4 Water and Barren Area B-8
3.2 Adjustment of Biomass Density B-9
3.2.1 Growing Season B-9
3.2.2 Layering of Forest Biomass B-9
3.3 Emission Factors B-ll
3.3.1 Canopy B-ll
3.3.2 Noncanopy B-12
3.4 Adjustment of Emission Factors B-13
3.4.1 Tingey Temperature and Solar Intensity Corrections B-13
3.4.2 Layered Correction Factors for Forest Biomass Classes B-15
3.5 Calculation of Biogenic Emissions B-15
3.6 Quality Control B-16
3.7 References B-16
B-2
-------
FIGURES
1 Biogenic Emissions Inventory System B-6
2 Agricultural Class Assignments B-9
TABLES
1 Vegetation Classes in the Biogenic Emissions Inventory System B-5
2 Forest Biomass Density Estimates B-7
3 Layers for Forest Biomass Classes B-10
4 Canopy Emission Factors at 30°C B-l 1
5 Noncanopy Emission Factors at 30°C and Estimated Percent Composition
of Emissions B-l2
6 Isoprene Temperature and Solar Intensity Adjustment Coefficients B-l3
7 Nonisoprene Temperature Adjustment Coefficients B-14
B-3
-------
EMISSIONS PROCESSING FOR THE
REGIONAL ACID DEPOSITION MODEL (RADM)
by
Beverly Goodrich
Christine Maxwell
Computer Sciences Corporation
Applied Technology Division
P.O. Box 12767
Research Triangle Park, NC 27711
BIOGENIC SOURCES
Hydrocarbon emissions influence the formation of acid deposition through the intricately-coupled
atmospheric chemistry of sulfur dioxide, nitrogen oxides, and reactive organic hydrocarbons. Biogenic
hydrocarbon emissions emanate from living surface vegetation-trees, shrubs, grasses, and agricultural
crops-and from decaying leaf litter and vegetation in fresh and salt water. Hydrocarbon emissions from
biogenic sources have been estimated to equal or exceed those from anthropogenic sources on a total-mass
basis. Thus, biogenic hydrocarbon emission rates have become an important input requirement for regional
acid deposition models such as the Regional Acid Deposition Model (RADM).
The calculation of biogenic hydrocarbon emission rates requires four basic components:
estimates of the biomass density of each vegetative class in each grid cell,
an adjustment of biomass density to account for season,
emission factors for the vegetation classes in the modeling region, and
empirical relationships that allow for adjusting the emission factors based on the values of specific
environmental parameters, such as temperature, solar intensity, soil conditions, and elevation.
DRAFT B-4 December 20,1990
-------
We show the procedure that we use to calculate the hourly grid-specific emissions for the RADM in
Figure 1, and describe it below. Our procedure provides the flexibility to update vegetation-specific emission
factors and allows for evaluating the importance of an individual vegetative species in the modeling domain.
We calculate the hourly emission rate for an individual grid cell and a specific hydrocarbon compound (or
group of compounds) by adjusting the vegetation-specific emission factors for canopy (forest) and
noncanopy (nonforest) areas to reflect variations in the meteorological episode being modeled, and then
summing the canopy and noncanopy emissions.
3.1 BIOMASS DENSITY BY VEGETATION CLASS
Data from the Oak Ridge National Laboratory (ORNL) Geoecology Data Base (Olson, 1980) form the basis
of the U.S. biogenic emissions inventory for the RADM. The database contains county-level land use data
for the classes of natural vegetation, agricultural crops, urban areas, and water. Table 1 lists examples of
vegetative species included in the biogenic emissions inventory system by vegetation class.
TABLE 1. VEGETATION CLASSES IN THE BIOGENIC EMISSIONS INVENTORY SYSTEM
Vegetation class
Examples
Natural vegetation:
Oak
Other deciduous
Coniferous
CANOPY (FOREST)
Oregon oakwoods, oak savanna, oak-hickory
Elm-ash, northern hardwoods, beech-maple
Cypress savanna, Douglas fir, conifer bog
Natural vegetation:
Scrubland
Grassland
Agricultural crops:
Urban area:
Water ("fresh and salt):
Barren area:
NONCANOPY (NONFOREST)
Creosote bush, chaparral, coastal sagebrush
Fescue-oatgrass, northern cordgrass, prairie
Alfalfa, barley, corn, cotton, hay, oats, peanuts, potatoes, rice, rye,
sorghum, soybeans, tobacco, wheat, miscellaneous crops
Urban grass, urban trees
Inland lakes
Tundra, ice, alpine meadows, desert
DRAFT
B-5
December 20,1990
-------
I A
Apportion county
data to grid cells;
reconcile data
Meteorology
processors
Processor
Calculate standard emissions:
correct and sum
Gridded
biogenic
emissions
Figure 1. Biogenic emissions inventory system.
The Landsat data set (Page, 1980), which reports data in standard NAPAP grid cells, and vegetation data
from Matthews (1984) form the basis of the Canadian biogenic emissions inventory. For the portion of
Canada south of 55° N latitude, we used the Landsat data set to determine the types of vegetation present by
land use class (Page, 1980). However, the Landsat data set contains no data for areas north of 55" N
latitude. If you require data north of this latitude, you can use the vegetation, land-use, and seasonal albedo
data sets of Matthews (1984). Note that the Matthews data specify only one vegetation type for each 1°
latitude by 1° longitude square.
DRAFT
B-6
December 20,1990
-------
3.1.1 Natural Vegetation Area
The ORNL Geoecology Potential and Adjusted Vegetation Data File uses Kuchler's vegetation codes
(001-106) to identify natural vegetation. We categorized these into the five natural vegetation classes: oak
forests, other deciduous forests, coniferous forests, scrubland, and grassland.
For Canada, we assigned the vegetation types within the Matthews (1984) and the Landsat (Page, 19SO) data
sets to one of the above five natural vegetation classes. We calculated the area allocated to each class in each
NAPAP grid cell. However, there was no direct correspondence to the oak class for either data set.
Therefore, we allocated a zero area to this class. 1
Oak, other deciduous, and coniferous forests are categorized as canopy (forest) vegetation classes. Canopy
emissions are determined by biomass density, a measure of the dry leaf biomass per unit area (kg/ha).
Table 2 presents the biomass density for the three canopy vegetation classes.
TABLE 2. FOREST BIOMASS DENSITY ESTIMATES
Forest biomass density (kg/ha) by canopy vegetation class
Forest biomass class
Deciduous high isoprene
Deciduous low isoprene
Deciduous nonisoprene
Coniferous nonisoprene
Oak
1,850
600
600
700
Other deciduous
600
1,850
900
1,350
Coniferous
390
260
260
5,590
Source: Lamb et al., 1987.
For a single canopy class, we use four forest biomass classes to describe the mix of forest vegetation,
including underwood, within that class. All oaks (and some other deciduous tree species) that emit more
than 10 Mgisoprene/Cgbiomass h) at temperatures near 30 °C are grouped together as high isoprene emitters.
All deciduous tree species with an emission rate less than 10 A«gisoprene/(gbiomass h) are considered low
isoprene emitters. Deciduous and coniferous tree species that do not emit isoprene make up the two
remaining forest biomass classes.
Natural vegetation areas of scrubland and grassland are noncanopy (nonforest) vegetation classes. For these
vegetation classes, as well as the agricultural crop class, we determine hydrocarbon emissions using emission
factors expressed as a function of land area. Thus, biomass density is not calculated directly.
1. A percentage of the deciduous areas could be allocated to the oak class if that percentage is known.
DRAFT B-7 December 20,1990
-------
3.1.2 Agricultural Crop Area
The agricultural crop data are from the ORNL Geoecology Crop Areas and Yields Data File. The crops
included in the biogenic emissions inventory are: alfalfa, barley, corn, cotton, hay, oats, peanuts, potatoes,
rice, rye, sorghum, soybeans, tobacco, wheat, and miscellaneous crops.
For Canada, neither the Matthews (1984) data set nor the Landsat (Page, 1980) data set assigned specific
agricultural classes. Therefore, we made agricultural class assignments along latitude and longitude lines
(see Figure 2), using cash crop data by province from atlases. Where only the broad crop category "grain" was
listed, the area was assigned 25% wheat and 75% oats, since oats, barley, and rye all have the same emission
factor.
Where wheat was specifically listed, the area was assigned 75% wheat and 25% oats. Where no specific crop
was listed, the area was assigned to the miscellaneous crops class.
3.1.3 Urban Area
The ORNL Geoecology Land Areas Data File specifies urban, rural, road, water, and federal land areas.
Urban areas include suburban areas if the same area has not been included as agricultural crops or natural
vegetation.
To account for hydrocarbon emissions from grass and trees in an urban area, we used the results from two
studies. Zimmerman (1979) showed that residential areas made up 14.6% of an urban area. Winer et al.
(1983) showed that trees covered 9.7% of an urban area, and that ground cover comprised more than 17.1%
of the area. For purposes of RADM modeling, we assume that 20% of an urban area is covered by grasses; a
further 20% is covered by trees, where this area is evenly distributed among oak, other deciduous, and
coniferous categories.
Note that the Matthews data set does not define urban areas.
3.1.4 Water and Barren Area
Water areas are also determined from the ORNL Geoecology Land Areas Data File (for the United States)
and the Landsat data set (for Canada). Oceans and the Great Lakes are not included in the biogenic
emissions inventory. However, smaller water areas such as lakes and rivers are included if the grid cell they
are in is not 100% water. Also included in the biogenic emissions inventory are any barren areas, such as
tundra, ice, alpine meadows, and desert. Areas of water and barren land are used only in reconciliation of the
total area for the county and the grid cell.
DRAFT B-8 December 20,1990
-------
50TN
30% Cora
10% Wktf
10% Oats
SD%Miic.~
50% Potatoes
50% Hay
Figure 2. Agricultural class assignments.
3.2 ADJUSTMENT OF BIOMASS DENSITY
3.2.1 Growing Season
We used first and last frost dates to determine the growing season for vegetation. We acquired these data
from (1) the ORNL Geoecology Growing Season Data File for the United States and (2) seasonal data for
Canada (Kaplan, N., U.S. Environmental Protection Agency, personal communication, January 4, 1989).
For simplicity, we assume that deciduous (i.e., nonconiferous) vegetation is at full biomass between the last
frost date and the first frost date and at zero biomass for the rest of the year. We assume that coniferous
vegetation is at full biomass over the entire year.
3.2.2 Layering of Forest Biomass
We vary the canopy biomass as a function of canopy height to simulate forest structure. We assume that
deciduous forest biomass classes (including high isoprene, low isoprene, and nonisoprene) have a canopy
height of 15 m, while the coniferous nonisoprene biomass class has a canopy height of 20 m.
Table 3 presents the height and the estimated fraction of biomass for each layer (B. Lamb, Washington State
University, personal communication, 1989).
DRAFT
B-9
December 20,1990
-------
TABLE 3. LAYERS FOR FOREST BIOMASS CLASSES
Fraction of biomass
Layer variable name Layer height, m by layer
DECIDUOUS (High Isoprene, Low Isoprene, Nonisoprene)
LAI 3.75 - 5.25 0.00
LA2 5.25 - 6.75 0.00
LAS 6.75 - 8.25 0.02
LA4 8.25 - 9.75 0.11
LAS 9.75 -11.25 0.22
LA6 11.25 -12.75 0.35
LA7 12.75 -14.25 0.22
LAS 14.25 -15.00 0.09
CONIFEROUS
LAP 5.0- 7.0 0.025
LAID 7.0- 9.0 0.050
LA11 9.0-11.0 0.150
LA12 11.0 -12.0 0.215
LA13 12.0 -15.0 0.215
LA14 15.0 -17.0 0.165
LA15 17.0 -19.0 0.120
LA16 19.0 - 20.0 0.050
Source: B. Lamb, Washington State University, personal communication, 1989.
DRAFT B-10 December 20,1990
-------
3.3 EMISSION FACTORS
Vegetation-specific emission factors are available for the following hydrocarbon compounds: isoprene,
Q-pinene, other identified monoterpenes (excluding a-pinene), and other unidentified hydrocarbons.
Emission rates of the unidentified hydrocarbons can be estimated. The reactivity of the unidentified hydro-
carbons is uncertain; we assume that about 95% of the unidentified compounds are reactive, and are evenly
split between terpenoid and oxygenated compounds.
3.3.1 Canopy
Table 4 lists the compound-specific emission factors [in units of Mgcompound/Cgbiomass h)] for the forest
biomass classes.
The canopy emission factors are standardized to 30 °C using the temperature relationship of Tingey (1981).
Each emission factor represents the geometric mean emission rate for a forest biomass class (B. Lamb,
Washington State University, personal communication, 1988).
The compound-specific emission factor by vegetation class (oak, other deciduous, or coniferous) is the
product of the forest biomass density for the vegetation class (from Table 2) and the canopy emission factor
for the hydrocarbon compound (from Table 4), summed over the four forest biomass classes.
TABLE 4. CANOPY EMISSION FACTORS AT 30 °C
Canopy emission factor,
iMgcorapound/
Hydrocarbon compound Forest biomass class (gbiomass n)]
Isoprene Deciduous high isoprene 14.69
Deciduous low isoprene 6.60
Deciduous nonisoprene 0.00
Coniferous nonisoprene 0.00
Q-pinene Deciduous high isoprene 0.13
Deciduous low isoprene 0.05
Deciduous nonisoprene 0.07
Coniferous nonisoprene 1.13
Other identified monoterpenes Deciduous high isoprene 0.11
Deciduous low isoprene 0.05
Deciduous nonisoprene 0.07
Coniferous nonisoprene 1.29
Other unidentified hydrocarbons Deciduous high isoprene 3.24
Deciduous low isoprene 1.76
Deciduous nonisoprene 1.91
Coniferous nonisoprene 1.38
Source: B. Lamb, Washington State University, personal communication, 1988.
DRAFT B-ll December 20,1990
-------
3.3.2 Noncanopv
Table 5 lists the noncanopy emission factors [pgcompound/(m2 h)], and the hydrocarbon compound-specific
emission composition (%) for the noncanopy vegetation classes.
Emission rates for a specific hydrocarbon compound can be calculated by multiplying the surface land area
(for each vegetation class) by the appropriate emission factor and the fraction of hydrocarbon compound
composition.
TABLE 5. NONCANOPY EMISSION FACTORS AT 30 °C AND ESTIMATED PERCENT
COMPOSITION OF EMISSIONS
Estimated emissions composition (%)
Noncanopy
vegetation
class
Natural Vegetation:
Grass
Scrub*
Agricultural Crops:
Alfalfa
Barley f
Corn
Cotton f
Hay
Oats f
Peanuts
Potatoes
Rice
Ryef
Sorghum
Soybeans
Tobacco
Wheat
Misc. crops f
Water: j
Barren Area: j
Noncanopy
emission factor,
iMScompound/
(m2.h)]
281.0
189.0
37.9
37.9
3,542.0
37.9
189.0
37.9
510.0
48.1
510.0
37.9
39.4
22.2
294.0
30.0
37.9
Isoprene
20
20
50
20
0
20
20
20
20
20
20
20
20
100
0
50
20
a-
pinene
25
25
10
25
10
25
25
25
25
25
25
25
25
0
10
10
25
Other
mono-
terpenes
25
25
10
25
10
25
25
25
25
25
25
25
25
0
10
10
25
Other
unidentified
hydrocarbons
30
30
30
30
80
30
30
30
30
50
30
30
30
0
80
30
30
Source: B. Lamb, Washington State University, personal communication, 1988.
* Emission factor is assumed to equal the hay emission factor.
f Emission factor is assumed to equal the alfalfa emission factor.
| Used only in the reconciliation of land area.
DRAFT
B-12
December 20,1990
-------
3.4 ADJUSTMENT OF EMISSION FACTORS
3.4.1 Tingev Temperature and Solar Intensity Corrections
Several studies have shown the effects of temperature and solar intensity on hydrocarbon emissions. We
adjust the gridded compound-specific emission factors for variations in temperature and solar intensity with
Tingey's curves (Tingey, 1981). Tingey's laboratory work with slash pine and live oak has yielded logarithmic
equations to describe the increase in isoprene emissions due to the combined effect of temperature and solar
intensity, and the increase in nonisoprene emissions due to temperature only. These equations are listed
below.
For isoprene emissions,
( E d\
JQ\ l-exp[-6(T-c)] /
F = F
*- ad; *- 30
where: E aa/ is the adjusted emission factor at temperature T [ jigisoprene/(gbiomass h) ],
£30 is the emission factor at 30 °C [ Mgisoprene/(gbiomass h) ], and
T is the hourly ambient temperature (°C), used as a surrogate for leaf temperature.
Table 6 lists the equation coefficients a, b, c, d, and e for four levels of light intensity (/uE/m2, where: pE
represents micro-einsteins, a unit of light energy). For light intensities not listed, we used linear interpola-
tion to calculate adjusted emission factors. Note that the biogenic emissions inventory system for the
RADM uses cloud cover data to attenuate light intensity values on an hourly basis.
TABLE 6. ISOPRENE TEMPERATURE AND SOLAR INTENSITY ADJUSTMENT COEFFICIENTS
Isoprene equation coefficient (unitless)
Light intensity
[M£/(m=.s)]*
800
400
200
100
a
1.200
0.916
0.615
0.437
b
0.400
0.239
0.696
0.312
c
28.30
29.93
32.79
31.75
d
0.796
0.462
0.077
0.160
e
1.00
1.95
4.75
10.73
Sources: Tingey, 1981, and Pierce et aL, 1990.
* /IE represents micro-einsteins, a unit of light energy.
DRAFT B-13 December 20,1990
-------
The coefficients for light intensity of 800 pE/m* were modified from Tingey's values to match a light
intensity of 400 jiE/m2 for temperatures of less than 29 °C. Also, the original Tingey equation expressed
emissions in terms of /igcarbon mass/dm2 leaf area; the isoprene equation presented above includes unit
conversions (68/60 represents the ratio of isoprene mass to carbon mass; 1.205 is the number of grams of
biomass per square decimeter of leaf area).
For nonisoprene emissions (o-pinene, other identified monoterpenes, and other unidentified hydrocarbons),
Eadi - £3o ' exp(a[T-30])
where: E adl is the adjusted emission factor at temperature 7 [ ^gn0nisoprene/(gbiomass * h) ],
E 30 is the emission factor at 30 °C [ Mgnonisoprene/(gbiomass h) ], and
T is the hourly ambient temperature (°C), used as a surrogate for leaf temperature.
The emission factor in Tingey's equation was expressed in units of /igcarbonmass/Cgbiomass n)-
nonisoprene equation presented above has been converted to units of Mgcompound/(gbiomass h) using the
ratio 136/120, i.e., the ratio of nonisoprene mass (as a-pinene)to carbon mass. Table 7 lists the coefficient a
of the nonisoprene adjustment equation by hydrocarbon compound.
TABLE 7. NONISOPRENE TEMPERATURE ADJUSTMENT COEFFICIENTS
Nonisoprene equation coefficient (unitless)
Hydrocarbon compound a
a-pinene 0.067
Other identified monoterpenes
(excluding o-pinene) 0.0739
Other unidentified hydrocarbons 0.0739
Source: Tingey, 1981 and Pierce et al, 1990.
DRAFT B-14 December 20,1990
-------
3.4.2 Layered Correction Factors for Forest Biomass Classes
A canopy model has been developed by the Laboratory for Atmospheric Research at Washington State
University (Gay, 1987). It is used to adjust emission factors for the four forest biomass classes (deciduous
high isoprene, deciduous low isoprene, deciduous nonisoprene, and coniferous nonisoprene).
Typical leaf biomass profiles are assumed for the deciduous and coniferous forest types (as discussed in
Section 3.2.2). The leaf area indices corresponding to these biomass profiles are apportioned into eight
vertical layers for each forest type.
The canopy model utilizes hourly meteorological data for the episode, including ambient temperature, solar
radiation, relative humidity, and wind speed. Meteorological input data are assumed to represent the top of
the canopy. Within each layer and for each of the two forest types, the canopy model uses an iterative
approach to compute the leaf-radiation balance of a typical leafs surface. Solar radiation is exponentially
reduced through the layers with the rate being a function of the biomass distribution. The rate of solar
attenuation increases more rapidly for the photosynthetically-active region of the solar spectrum than for the
rest of the spectrum, since leaves preferentially absorb visible light (Baldocchi et al., 1984).
Both the total solar spectrum and the visible spectrum subset are calculated over the eight layers of the
hypothetical canopies. The calculated total solar radiation is used to compute the leaf temperature at each
level using the radiation balance equation of Gates and Papian (1971).
The final output from this process consists of leaf temperatures and photosynthetically-active radiation for
the eight layers in the two forest types. We then use these data when applying the Tingey correction factors.
3.5 CALCULATION OF BIOGENIC EMISSIONS
For the forest biomass classes, we multiply the layered biomass by the canopy emission factors to arrive at the
layered standardized emissions. These emissions are then adjusted by the layered Tingey correction factors
and we sum the results to produce canopy emissions.
For the noncanopy vegetation classes, we multiply the biomass area by the noncanopy emission factors to
arrive at the standardized emissions. These emissions are then adjusted using the Tingey curves to produce
noncanopy emissions. The canopy and noncanopy emissions are then summed for each grid cell.
DRAFT B-15 December 20,1990
-------
3.6 QUALITY CONTROL
Quality control efforts by the EPA have focused on reconciling land area values. The sum of the areas
allocated to all the vegetation classes in a county (natural vegetation, agricultural crops, urban, water, and
barren areas) must equal the total area of the county. Similarly, the sum of the areas allocated to all the
vegetation classes in one grid cell must equal the total area of the grid cell. Thus, we account for all the land
area in a county or grid cell.
New or revised emission factors resulting from further studies of hydrocarbon emissions from vegetative
species will be incorporated into the biogenic emissions inventory system.
3.7 REFERENCES
Baldocchi, D.D., D.R. Matt, B.A. Hutchison, and R.T. McMillen. 1984. Solar radiation within an
oak-hickory forest: an evaluation of the extinction coefficients for several radiation components
during fully-leafed and leafless periods. Agricultural and Forest Meteorology, 32:307-322.
Gates, D. and L. Papian. 1971. Atlas of Energy Budgets on Plant Leaves. Academic Press, New York, New
York. pp. 1-16.
Gay, D. 19S7. A National Inventory of Biogenic Hydrocarbon Emissions Based Upon a Simple Forest Canopy
Model. M.S. Thesis, Washington State University, Pullman, WA 73pp.
Lamb, B., A Guenther, D. Gay, and H. Westberg. 1987. A national inventory of biogenic hydrocarbon
emissions. Atmospheric Environment, 21(8):1695-1705.
Lamb, B., D. Gay, H. Westberg, and E. Allwine, 1990. Development of a National Inventory for Natural
Hydrocarbon Emissions. Invited paper presented at the NAPAP 1990 International Conference on
"Acidic Deposition: State of Science and Technology," February 11-16,1990, Hilton Head Island, SC.
National Acid Precipitation Assessment Program, Washington, DC.
Matthews, E. 1984. Vegetation, Land Use and Seasonal Albedo Data Sets: Documentation of Archived Data
Tape. NASA Technical Memorandum 86107, National Aeronautics and Space Administration,
Goddard Space Flight Center, Institute for Space Studies. New York, New York.
Olson, R.J. 1980. Geoecology: A County-Level Environmental Data Base for the Conterminous United States.
Publication No. 1537, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge,
Tennessee.
DRAFT B_16 December 20,1990
-------
Page, S.H. 1980. National Land Use and Land Cover Inventory. Lockheed Engineering and Management
Services Co., Inc., Remote Sensing Laboratory. Las Vegas, Nevada.
Pierce, T.E., B.K. Lamb, and A.R. Van Meter, 1990. Development of a Biogenic Emissions Inventory System
for Regional Scale Air Pollution Models. Paper number 90-94.3 presented at the 83rd Air and Waste
Management Association Annual Meeting, Pittsburgh, PA, June 24 - 29,1990.
Tingey, D.T. 1981. The effect of environmental factors on the emission of biogenic hydrocarbons from live
oak and slash pine. In: J.J. Bufalini and R.R. Arnts (eds.), Atmospheric Biogenic Hydrocarbons, Vol. 1,
Emissions. Ann Arbor Science, Ann Arbor, Michigan.
Winer, A.M., D.R. Fritz, P.R. Miller, R. Atkinson, D.E. Brown, W.P.L. Carter, M.C. Dodd, C.W. Johnson,
M.A. Myers, K. Neisess, M.P. Poe, and E.R. Stephens. 1983. Investigation of the Role of Natural
Hydrocarbons in Photochemical Smog Formation in California. Final Report AO-056-32, California
Air Resources Board, Statewide Air Pollution Research Center, University of California, Riverside,
California.
Zimmerman, P.R. 1979. Determination of Emission Rates of Hydrocarbons from Indigenous Species of
Vegetation in the Tampa/St. Petersburg, Florida Area. EPA 904/9-77-028, U.S. Environmental
Protection Agency.
DRAFT B-17 December 20,1990
-------
APPENDIX C
GRID PLOTS OF INTERPOLATED
METEOROLOGICAL DATA FOR SELECT MONTHS
C-l
-------
Monthly average temperature for January
-26.7 to -10
Figure C-1.
-------
deg . C
D -8 . 4 to 0
0 to 5
5 to 10
10 to 15
15 to 20
20 to 24.6
Figure C-2.
-------
Monthly average temperature for Ju
Figure C-3.
-------
deg . C
D -1.2 to 0
D 0 to 5
5 to 10
10 to 15
15 to 20
20 to 26.3
Figure C-4.
-------
Monthly average attenuated visible solar radiation for January
uE/sq m/sec
29.3 to 200
200 to 250
250 to 300
300
350
to 350
to 400
400 to 450.6
Figure C-5.
-------
-------
Monthly average attenuated visible solar radiation for July
uE/sq m/sec
D430.8 to 500
D 500 to 550
550 to 600
600 to 650
650 to 700
700 to 741.2
Figure C-7.
-------
uE/sq m/sec
D 74.8 to 250
D250 to 300
300 to 350
350 to 400
400 to 450
450 to 539.4
Figure C-8.
-------
Monthly average sky cover for January
Figure C-9.
-------
tenths
D 0 to 4
D 4 to 4.5
4.5 to 5
5 to 5.5
5.5 to 6
6 to 8.9
Figure C-10.
-------
Monthly average sky cover for July
tenths
D 0 to 4
G3 4 to 5
to 5.5
to 8 . 4
Figure C-11.
-------
6 to 7
7 to 9.1
Figure C-12.
-------
Monthly average wind speed for January
ml s e c
D 0.09 to 1
D 1 to 2
2 to 4
4 to 5
5 to 7
7 to 10.9
Figure C-13.
-------
ml s e c
0.1 to 1
to 9.6
Figure C-14.
-------
Monthly average wind speed for July
Figure C-15.
-------
to 10.7
Figure C-16.
-------
Monthly average relative humidity for Januar
decimal fraction
DO to 0.2
D 0.2 to 0.4
to 0.6
to 0.7
4
6
0.7 to 0.8
0.8 to 0.94
Figure C-17.
-------
dec imaI f r a c t
to 0.2
2 to 0.4
4 to 0.5
5 to 0.6
0.6 to 0.8
0.8 to 0.94
on
Figure C-18.
-------
Monthly average relative humidity for Ju
dec imaI fraction
DO to 0.2
D 0. 2 to 0.4
0.4 to 0.6
0.6 to 0.7
0.7 to 0.8
0.8 to 0.97
Figure C-19.
-------
decimal fraction
DO to 0.3
D 0.3 to 0
0.4 to 0
0.6 to 0
0.7 to 0.8
0.8 to 0.91
Figure C-20.
-------
APPENDIX D
BIOGENIC EMISSIONS AND SOLAR RADIATION
SOURCE CODE LISTINGS
Program Name Page
SOLENGY.FORT D-2
BIOMASS.SAS D-ll
CORRECT2.SAS D-16
RADMBIO.SAS D-25
D-l
-------
SOLENGY.FORT
D-2
-------
PROGRAM SOLENGY
f^^^^^^^^^^^^^&^&^^^^&^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^'fc'jf'
C
C THIS ROUTINE GENERATES A FILE OF HOURLY GRIDDED SOLAR ENERGY
C GIVEN THE SUN'S ZENITH ANGLE AND A RANGE OF SOLAR WAVELENGTHS.
C
C INPUT FILES:
C
C CONTROL FILE TO WITH YEAR STARTMONTH DAY NHOURS STARTHOUR (CNTRL)
00000200
00000300
i-nonoAA fin
*UUUUU1UU
00000500
00000600
00000700
00000800
00000900
00001000
00001100
C SOLAR ENERGY AS A FUNCTION OF ZENITH ANGLE AND WAVELENGTH (SOLARFLUX) 00001200
C LAT & LONG OF THE CENTERPOINTS OF THE RADM GRID CELLS (LATLON)
C (NOTE: LONGITUDE IS NEGATIVE FOR WESTERN HEMISPHERE IE. N. AMERICA)
C
C
C OUTPUT FILE:
C
C HOURLY GRIDDED SOLAR ENERGY (SOLAR)
C - IN TWO FORMATS: MICROEINSTEINS/M2-SEC & CAL-GM/CM2-SEC
C
00001300
00001400
00001500
00001600
00001700
00001800
00001900
00002000
00002100
C 2/89 -BRG- BASED ON THE ROM PROGRAM, MODIFIED TO WORK WITH THE RADM GRID00002200
C argument list description:
C
C INPUT ARGUMENTS:
C
C PRES SURFACE AIR PRESSURE (ASSUMED TO BE 980 MB)
C IMONTH - MONTH (FROM 1 TO 12)
C IDAY DAY OF THE MONTH (1 - 31)
C I YEAR - YEAR (SUCH AS 88)
C IHOUR - LOCAL STANDARD TIME (1 - 24)
C LAT - LATITUDE (DEGREES)
C LONG - LONGITUDE (DEGREES)
C
C OUTPUT ARGUMENTS:
C
C TOTAL - TOTAL SOLAR RADIATION, DIFFUSE AND DIRECT (LY/MIN)
C PAR - VISIBLE SOLAR RADIATION (UE/M**2-S)
C
C INTERNAL ARGUMENTS:
C
C DIRCTO - DIRECT INCIDENT SOLAR RADIATION (W/M**2)
C A - SOLAR CONSTANT AT SEA-LEVEL, VARIES BY DAY (W/M**2)
C ADAY - FIXED VALUES OF A USED IN THE TABLE LOOK UP
C B - INVERSE AIR MASS, VARIES BY DAY (ATM**-1)
C BDAY - FIXED VALUES OF B USED IN THE TABLE LOOK UP
C PRESO - STD SEA-LEVEL PRESSURE (1013 MB)
C ZENITH - ZENITH ANGLE COMPUTED AS FUNCTION OF JULIAN DAY, TIME
C TIME ZONE, LAT, AND LONGITUDE. (RADIANS)
C DFUSE - DIFFUSE SOLAR RADIATION (W/M**2)
C C - CONSTANT WHICH ACCOUNTS FOR WATER VAPOR, VARIES BY
C JULIAN DAY (UNITLESS)
C CDAY - FIXED VALUES OF C USED IN THE TABLE LOOK UP
C IDAY - FIXED VALUES OF JULIAN DAY CORRESPONDING TO ADAY,
00002210
00002220
00002230
00002240
00002250
00002260
00002270
00002280
00002290
00002291
00002292
00002293
00002294
00002295
00002296
00002297
00002298
00002299
00002300
00002301
00002302
00002303
00002304
00002305
00002306
00002307
00002308
00002309
00002310
00002311
00002312
00002313
D-3
-------
c
c
c
c
c
c
c
c
c
c
c
c
BDAY, AND CDAY 00002314
WM2LY CONVERSION OF W/M**2 TO LY/MIN (0.001433) 00002315
LY2UE - CONVERSION OF LY/MIN TO UE/M**2-S, ASSUMES THAT 00002316
VISIBLE PORTION OF SPECTRUM IS 400 NM TO 700 NM 00002317
AND THE REPRESENTATIVE WAVELENGTH IS 500 NM 00002318
THUS USED ONLY FOR PAR (2916.) 00002319
DAYINC - DAY INCREMENT USED IN INTERPOLATING BETWEEN DAYS 00002320
EXPA EXP FUNCTION WITH ZENITH ANGLE AND AIR MASS 00002321
CATTEN CLOUD ATTENUATION (UNITLESS), FROM 0 TO 1 00002322
ANGLE - SOLAR ANGLE (DEGREES) 00002323
DG2RD CONVERSION OF DEGREES TO RADIANS (0.0174533) 00002324
00002325
C***********************************************************************00002330
00002400
IMPLICIT NONE 00002500
00002600
CHARACTER*!2 INFILE, OUTFILE 00002700
00002800
INTEGER*4 YEAR, SMTH, SDAY, STHR, NHRS, ICOL, IROW, IHR, IWAVE1, 00002900
& IWAVE2, M, J, I, JMO(12), JDAY, NCOL, NROW, OCOL, OROW, 00003000
& EOF, HOUR
C SET UP FOR 300X210 NAPAP GRID WITH ORIGIN AT 1,1
PARAMETER (NCOL=300,NROW=210,OCOL=1,OROW=1)
Following two lines commented out 03/01/91 Shannon L. Parker
C
C.
C
REAL*4 WAVE, WAVE1, WAVE2, FLUXC(52,10) , FLUXE(52,10), ZENITH,
& FCTOT, FETOT, Z(10), ZX, FC, FE
REAL*4 WAVE, WAVE1, WAVE2, ZENITH,
& total, par, Z(10), ZX, FC, FE
REAL*4 LAT(300,210), LON(300,210)
PARAMETER (WAVE = 290.0, WAVE! = 400.0, WAVE2 = 690.0)
DATA INFILE /'LATLON'/
DATA OUTFILE /'SOLAR'/
DATA Z /O.,10.,20.,30.,40.,50.,60.,70.,78.,86./
DATA JMO/0,31,59,90,120,151,181,212,243,273,304,334/
.OTHER DECLARATIONS
REALDRCTO, CN, A, ADAY(14), B, BDAY(14), PRES, PRESO, ZENITH,
& C, CDAY(14), DFUSE, TOTAL, PAR, LY2UE, WM2LY, DG2RD,
& DAYINC, EXPA, CATTEN, ANGLE
INTEGER NDAY(14), JDAY, I
DATA NDAY/ 1, 21, 52, 81,112,142,173,
& 203,234,265,295,326,356,366/
DATA ADAY/1203.,1202.,1187.,1164.,1130.,1106.,1092 . ,
& 1093.,1107.,1136.,1136.,1190.,1204.,1203./
00003100
00003200
00003300
00003400
00003500
00003600
00003610
00003620
00003700
00003800
00003900
00003910
00003920
00003930
00004000
00004100
00004200
00004300
00004400
00004500
00004600
00004700
00004800
00004810
00004820
00004830
00004840
00004850
00004860
00004870
00004880
00004890
00004891
00004892
D-4
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
DATA BDAY/.141, .141, .142, .149, .164, .177, .185,
& .186,. 182 ,.165 ,.152, .144, .141..141/
DATA CDAY/.103, .103, .104, .109, .120, .130, .137,
& .138 ,.134,. 121,. 111,. 106,. 103, .103/
DATA WM2LY/0.001433/, LY2UE/2916 ./, CN/1./,
& DG2RD/0.0174533/, PRESO/1013 ./, PRES/980./
ELEV = 0.
READ PARAMETERS FROM CONTROL RECORD.
YEAR - START YEAR
SMTH - START MONTH
SDAY - START DAY
STHR = START HOUR
NHRS - NUMBER OF HOURS TO PROCESS
OPEN (UNIT = 11, FORM=' FORMATTED' , STATUS= ' OLD ' , ACTION='READ'
READ (11,500,ERR=400) YEAR, SMTH, SDAY, NHRS, STHR
PRINT *, YEAR, SMTH, SDAY, STHR, NHRS
COMPUTE JULIAN DAY FORMAT USING JULIAN ROUTINE.
JDAY = SDAY + JMO(SMTH)
IF (MOD(YEAR,4).EQ.O.AND.SMTH.GT.2) JDAY = JDAY + 1
READ THE SOLAR ENERGY CONSTANTS
CALL FLXEIN ( FLUXC , FLUXE )
COMPUTE START/END WAVELENGTH INDEX.
THE WAVELENGTH INDEX RANGES FROM 1 TO 52.
00004893
00004894
00004895
00004896
00004897
00004898
00004899
00004900
00005000
00005100
00005200
00005300
00005400
00005500
00005600
00005700
) 00005800
00005900
00006000
00006100
00006200
00006300
00006400
00006500
00006600
00006700
00006800
00006900
00007000
00007100
00007200
00007300
00007400
Following two lines commented out 03/01/91 Shannon L. Parker 00007410
IWAVE1 = (WAVE1 - WAVE) / 10.0 + 1
IWAVE2 = (WAVE2 - WAVE) / 10.0 +1
READ IN ALL RECORDS - LAT & LON FOR 300X210
OPEN(UNIT = 10, FORM=' FORMATTED' , STATUS= ' OLD ' ,ACTION='READ'
READ(10,100,IOSTAT=EOF) ICOL, IROW, LAT(ICOL, IROW) , LON(ICOL
DO WHILE (EOF.NE.-l)
READ(10,100,IOSTAT=EOF) ICOL, IROW, LAT (I COL, IROW) , LON(ICOL
END DO
CLOSE (10)
OPEN (UNIT = 12, FORM= ' UNFORMATTED ' ,STATUS=' NEW')
& RECL-6)
********* THE HOUR LOOP STARTS HERE.
IHR = STHR ! STARTING HOUR
HOUR = 1 ! NUMBER THE HOURS FROM 1 TO NHRS
DO WHILE (NHRS .GT. 0)
00007500
00007600
00007700
00007800
00007900
) 00008000
, IROW) 00008100
00008200
, IROW) 00008300
00008400
00008500
00008600
00008700
00008800
00008900
00009000
00009100
00009200
00009300
00009400
D-5
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c.
c
10
c
20
INTERPOLATE FOR THE SOLAR ENERGY GIVEN WAVELENGTH AND ZENITH ANG.
DO IROW = OROW.NROW + OROW - 1
DO ICOL = OCOL.NCOL + OCOL - 1
CALCULATE THE ZENITH ANGLE FOR THIS HOUR
CALL SOLRADT(JDAY,IHR,LAT(ICOL,IROW),LON(ICOL,IROW).ZENITH)
IF ZENITH ANGLE IS OUT OF RANGE OF TABLE, THEN DON'T CALCULATE
ANY SOLAR FLUX - ELSE GET THE SOLAR ENERGY IN BOTH SETS OF UNITS
FIND THE RANGE THE ZENITH ANGLE IS IN
J = 2
DO WHILE (ZENITH .GE. Z(J) .AND. J .LT. 10)
J = J + 1
END DO
ZX = (ZENITH - Z(J-l)) / (Z(J) Z(J-l))
INITIALIZE THE SOLAR ENERGY TO ZERO
Shannon L. Parker
CHANGED 03/01/91
FETOT =0.0
FCTOT =0.0
PAR =0.0
total =0.0
LOOP THROUGH ALL WAVELENGTHS, CALCULATING ENERGY AT EACH
WE WILL BE USING 400 NM TO 690 NM WAVELENGTHS
DO M = IWAVE1,IWAVE2
FC = FLUXC(M.J-l) + (FLUXC(M.J) - FLUXC(M,J-l)) * ZX
FE = FLUXE(M.J-l) + (FLUXE(M.J) - FLUXE(M,J-l)) * ZX
IF (FC .GT. 0.0) FCTOT = FCTOT + FC
IF (FE .GT. 0.0) FETOT = FETOT + FE
END DO
Comput radiation section added 03/01/91 Shannon L. Parker
.COMPUTE DIRECT RADIATION
FIRST, PERFORM THE TABLE LOOK UP
DO 10 I = 1, 14
IF (JDAY .LE. NDAY(I)) GO TO 20
CONTINUE
PRINT *,'ERROR IN TABLE LOOKUP, JDAY OUT OF RANGE'
STOP
IF (I .LT. 1 .OR. I .GT. 14) THEN
PRINT *.'ERROR, DAY INDEX OUT OF RANGE'
STOP
ENDIF
00009500
00009600
00009700
00009800
00009900
00010000
00010100
00010200
00010300
00010400
00010500
00010600
00010700
00010800
00010900
00011000
00011100
00011200
00011300
00011400
00011500
00011600
00011700
00011710
00011720
00011800
00011900
00011901
00011910
00011920
00012000
00012100
00012200
00012300
00012400
00012500
00012600
00012700
00012800
00012900
00012910
00012911
00012920
00012930
00012940
00012950
00012960
00012970
00012980
00012990
00012991
00012992
00012993
00012994
D-6
-------
c
c.
c
c
c
c
c
c
IF (NDAY(I) .EQ. 1) THEN
A - ADAY(l)
B - BDAY(l)
C - CDAY(l)
ELSE
DAYINC = FLOAT(JDAY-NDAY(I 1))/FLOAT(NDAY(I)-NDAY(I-l))
A - ADAY(I-l) + (ADAY(I)-ADAY(I-1))*DAYINC
B - BDAY(I-l) -I- (BDAY(I)-BDAY(I-1))*DAYINC
C - CDAY(I-l) + (CDAY(I)-CDAY(I-1))*DAYINC
ENDIF
.CHECK RANGE OF EXP
IF (PRES .LT. 100.) STOP 'ERROR IN SFC PRES, ITS TOO LOW
IF (ZENITH .GT. 1.55) THEN
EXPA - 0.
ELSE
EXPA - EXP(-B*(PRES/PRESO)/COS(ZENITH))
ENDIF
DRCTO - CN*A*EXPA
DFUSE -= C*DRCTO
TOTAL = DRCTO*COS(ZENITH) + DFUSE
TOTAL = TOTAL*WM2LY
.VISIBLE IS ASSUMED TO CONSIST OF 50% OF THE TOTAL
PAR = TOTAL*0.5*LY2UE
WRITE OUT THE AMOUNTS FOR THIS HOUR & GRID CELL
***Following line changed 03/01/91 Shannon L. Parker
WRITE(12) ICOL, IROW, HOUR, FCTOT, FETOT
WRITE(12) ICOL, IROW, HOUR, total, par
END DO
END DO
PREP FOR NEXT HOUR AND CHECK FOR A CHANGE OF DAY.
PRINT 510, JDAY, IHR
IHR = IHR + 1
HOUR - HOUR + 1
NHRS = NHRS - 1
IF (IHR .GT. 23) THEN
IHR = 0
JDAY - JDAY + 1
ENDIF
C
C **** END OF HOUR LOOP
C
END DO
PRINT 502
CLOSE (11)
STOP
C
c
c
400
401
ERROR PROCESSING
PRINT 503
CALL EXIT
PRINT 504
00012995
00012996
00012997
00012998
00012999
00013000
00013001
00013002
00013003
00013004
00013005
00013006
00013007
00013008
00013009
00013010
00013011
00013012
00013013
00013014
00013015
00013016
00013017
00013018
00013020
00013030
00013100
00013110
00013200
00013300
00013400
00013500
00013600
00013700
00013800
00013900
00014000
00014100
00014200
00014300
00014400
00014500
00014600
00014700
00014800
00014900
00015000
00015100
00015200
00015300
00015400
00015500
00015600
00015700
D-7
-------
402
C
100
500
502
503
504
505
507
508
510
CALL EXIT
PRINT 505
CALL EXIT
FORMAT(1X,I4,I4,F9.3,F9.3)
FORMAT(5(I5))
FORMAT(IX,'PROCESSING COMPLETE. ')
***ERROR*** READING CONTROL RECORD')
EOF ENCOUNTERED READING SOLAR ENERGY FILE')
***ERROR*** READING SOLAR ENERGY FILE')
FORMAT(IX
FORMAT(IX
FORMAT(IX
FORMAT()
FORMATC '.10F11.5)
FORMAT(IX,'...Data written for DAY
END
,15,' HOUR ' ,12,' . . . ')
03/01/91
C ***Following subroutine commented out
C
SUBROUTINE FLXEIN(FLUXC,FLUXE)
C******************************^
C
00015800
00015900
00016000
00016100
00016200
00016300
00016400
00016500
00016600
00016700
00016800
00016900
00017000
00017100
00017200
Shannon L. Parker 00017210
00017220
00017300
00017400
00017500
THIS PROGRAM CONVERTS PETERSON'S ACTINIC FLUX UNITS FROM
PHOTONS/CM2-SEC TO MICOREINSTEINS/M2-SEC & TO LANGLEY-MIN
(CAL-GM/CM2-SEC)
NOTE: AMOUNTS IN FLUX DATA FILE NEED TO MULTIPLIED BY 10E15
CHARACTER*!2 INFILE
CONVERSION E:
CONVERSION C:
00017600
00017700
00017800
00017900
00018000
00018100
00018200
00018300
00018400
00018500
00018600
00018700
(PHOTONS/CM2-SEC) / (6.02252E17 PHOTONS/MICROEINSTIN) 00018800
* (1.0E4 CM2/M2) 00018900
= MICOREINSTEINS/M2-SEC 00019000
00019100
(PHOTONS/CM2-SEC) * (.2389 CAL/J) * (6.63E-34 JSEC/PH000019200
00019300
00019400
00019500
DATA A/6.02252E17/, B/1.0E4/, C/2.851E-15/ 00019600
DATA INFILE/'SOLARFLUX'/ 00019700
00019800
00019900
00020000
00020100
00020200
00020300
00020400
00020500
00020600
00020700
00020800
00020900
C
C
C
C
C
c************************************************************
C REAL*4 XJ(52,10), WL, A, B, C, FLUXC(52,10), FLUXE(52,10)
C
C INTEGER*4 I, J, K
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C100
C
C
C
C
* (3E10 CM/SEC) * (60SEC/MIN) / (WAVELENGTH CM)
= LANGLEY-MIN OR CAL-GM/CM2-MIN
READ ACTINIC FLUXES
OPEN(UNIT -= 14, FORM='FORMATTED',STATUS='OLD',ACTION='READ')
DO 1=1,52
READ(14,100) (XJ(I,J), J-1,10)
FORMAT(10F10.7)
END DO
CLOSE (14)
CONVERT FLUX TO BOTH SETS OF UNITS
D-8
-------
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
!DETERMINE WAVELENGTH IN NM
1.0E15 / A * B
1.0E15 * C / WL
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
DO K-1,10
DO J-1,52
WL - 290 + (J-l) * 10
FLUXE(J.K) = XJ(J.K) *
FLUXC(J,K) = XJ(J.K) *
END DO
END DO
RETURN
END
SUBROUTINE SOLRADT(JDAY, HR, DLAT, DLON, ZEANGL)
THIS SUBROUTINE CALCULATES THE SOLAR ANGLES GIVEN A
PARTICULAR LOCATION AND TIME OF YEAR. THE METHOD USED
IS THAT PRESENTED BY HOLTSLAG AND VAN ULDEN (1983).
IT THEN CALCULATES THE SOLAR RADIATION AT THE GROUND
FOR VARIOUS TIMES OF THE YEAR AND LOCATIONS. THIS SHCEME
WAS ADAPTED FROM "P15G" OF ROM AFTER KONDRATYEV, (1969).
2/89 - BRG MODIFIED TO BETTER INTERFACE WITH THE CALCULATION OF
SOLAR INTENSITY FOR THE RADM GRID
******************************************
IMPLICIT NONE
REAL*4 SINLAT, COSLAT, COSHR, SINDEC, COSDEC
REAL*4 RAD2, SCLHGT, RADIUS, SCH2, RSX2
PARAMETER (SCLHGT=8000., RADIUS=6.37E+06)
PARAMETER (RAD2=RADIUS*RADIUS, SCH2=SCLHGT*SCLHGT)
PARAMETER (RSX2=RADIUS*SCLHGT*2.0)
INTEGER*4 JDAY, HR
REAL*4 RJDAY, HOUR, LAT, LON, ZEANGL, DLAT, DLON
REAL*4 PI, SOLDEC, HRANGL, SOLELV, SL
PARAMETER (PI-3.14159265)
REAL*4 DG2RAD, RAD2DG
PARAMETER (DG2RAD=PI/180., RAD2DG=180./PI)
********************************************
SUBROUTINE PARAMETERS
JDAY - JULIAN DATE TO PROCESS
HR - HOUR TO PROCESS IN GMT
DLAT - LATITUDE OF THIS CELL IN DEGREES
DLON - LONGITUDE OF THIS CELL IN DEGREES
WESTERN HEMISPHERE (IE. NORTH AMERICA) IS NEGATIVE
ZEANGL - ZENITH ANGLE IN DEGREES
DEFINITIONS USED BY SOLAR RADIATION ROUTINES
PI - CONSTANT "PI"
DG2RAD - CONSTANT TO CONVERT DEGREES TO RADIANS
RAD2DG - CONSTANT TO CONVERT RADIANS TO DEGREES
SOLDEC - SOLAR DECLINATION ANGLE IN RADIANS
00021000
00021100
00021200
00021300
00021400
00021500
00021600
00021700
00021800
00021900
00022000
00022100
00022200
00022300
00022400
00022500
00022600
00022700
00022800
00022900
00023000
00023100
00023200
00023300
00023400
00023500
00023600
00023700
00023800
00023900
00024000
00024100
00024200
00024300
00024400
00024500
00024600
00024700
00024800
00024900
00025000
00025100
00025200
00025300
00025400
00025500
00025600
00025700
00025800
00025900
00026000
00026100
00026200
00026300
D-9
-------
C HRANGL - SOLAR HOUR ANGLE IN RADIANS
C SOLELY - SOLAR ELEVATION IN RADIANS
C
C ********************************************
C SET THE DAY AND HOUR INTO REAL NUMBERS
C
RJDAY = JDAY
HOUR - HR
C
C TO CSLCULATE THE ZENITH ANGLE, THE LAT & LON NEED TO BE RADIANS
C
LAT - DLAT * DG2RAD
LON = -DLON * DG2RAD
C CALCULATE SOLAR LONGITUDE
SL - 4.871 + DG2RAD*RJDAY + 0.033*SIN(DG2RAD*RJDAY)
C CALCULATE SOLAR DECLINATION
SOLDEC - ASIN(0.398*SIN(SL))
C CALCULATE HOUR ANGLE
HRANGL = -LON + 0.043*SIN(2*SL) -
& 0.033*SIN(DG2RAD*RJDAY) + 0.262*HOUR - PI
C CALCULATE VARIOUS TRIG FUNCTION VALUES
SINLAT = SIN(LAT)
COSLAT = COS(LAT)
COSHR = COS(HRANGL)
SINDEC = SIN(SOLDEC)
COSDEC = COS(SOLDEC)
C CALCULATE THE SOLAR ELEVATION
SOLELV = ASIN( SINDEC*SINLAT -I- COSDEC*COSLAT*COSHR )
C CALCULATE THE ZENITH ANGLE
ZEANGL = PI/2.0 - SOLELV
C RETURN THE ZENITH ANGLE IN DEGREES
ZEANGL = ZEANGL * RAD2DG
RETURN
END
00026400
00026500
00026600
00026700
00026800
00026900
00027000
00027100
00027200
00027300
00027400
00027500
00027600
00027700
00027800
00027900
00028000
00028100
00028200
00028300
00028400
00028500
00028600
00028700
00028800
00028900
00029000
00029100
00029200
00029300
00029400
00029500
00029600
00029700
00029800
00029900
00030000
00030100
00030200
00030300
00030400
00030500
00030600
00030700
00030800
00030900
00031000
00031100
00031200
00031300
D-10
-------
BIOMASS.SAS
D-ll
-------
**********************************************^
* RUNSTREAM TO EXECUTE PROGRAM BIO.BIOMASS.SAS
*
*
>v
*
*
BIOMASS.SAS SELECTS MONTH SPECIFIC BIOMASS FOR CANOPY
VEGETATION LAND AREA FOR NONCANOPY VEGETATION AND CALCULATES
THE GRIDDED BIOMASS AND LAND AREA FOR EACH MONTH USING GROWTH
FACTORS. THE INPUT/OUTPUT FILES ARE:
*
*
*
*
*
*
*
INI: THE EPISODE TO BE RUN (THE MONTH TO CALC BIOMASS FOR)
IN2: GROWTH FACTORS FOR NONCANOPY VEGETATION
IN3: BIOMASS GROWTH FACTORS FOR CANOPY (FOREST) VEGETATION
IN4: GRID ORIGIN AND BOUNDARIES
INS: GRIDDED CANOPY VEGETATION AREA
IN6: GRIDDED URBAN TREE AREA
IN7: GRIDDED NONCANOPY VEGETATION AREA
OUT1: BIOMASS FOR CANOPY
OUT2: BIOMASS FOR URBAN TREES
OUT3: LAND COVERAGE FOR NONCANOPY
r****-1"
OPTIONS SOURCE MPRINT;
* BIOMASS.SAS;
CALCULATES THE BIOMASS FOR CANOPY, NON-CANOPY, AND URBAN
TREES.
* LGM 7/89 IBM/TSO VERSION;
* BRG 6/89 CMS VERSION;
* BRG 5/89 BIOGENIC EMISSIONS PROCESSING VERSION 2.1;
--'- REVISED CANOPY MODEL FOR RADM;
* BRG 3/89 BIOGENIC EMISSIONS PROCESSING VERSION 2.0;
* IMPLEMENTATION OF THE CANOPY MODEL;
* BRG 12/88 BIOGENIC EMISSIONS PROCESSING VERSION 1.0;
I CALCULATE BIOMASS FOR SPECIFIC MONTH
]
] CALCULATE THE BIOMASS AMOUNTS BY TYPE OR BIOMASS AREAS BY
] TYPE. FIND THIS EPISODE'S GROWTH FACTORS & BIOMASS FACTORS
* ,>
DATA EPISODE;
SET INI.EPISODE;
LENGTH MONTH 4.;
OKEEP MONTH;
PUT 'EPISODE MONTH IS ' MONTH;
DATA GROWTH; * GET APPRO MONTH'S GROWTH FACTORS;
MERGE EPISODE(IN=INEPS) LN2.NCBIOFC;
BY MONTH;
LENGTH DEFAULT=4;
IF INEPS;
DATA CNPYBF; * GET APPRO MONTH'S BIOMASS FACTORS-FOR FOREST;
MERGE EPISODE(IN=INEPS) IN3.CNPBIOFC;
BY MONTH;
*****00000100
00000200
00000300
00000400
00000500
00000600
00000700
00000800
00000900
00001000
00001100
00001200
00001300
00001400
00001500
00001600
00001700
00001800
00001900
00002000
00002100
00002200
00002300
00002400
00002500
00002600
00002700
00002800
00002900
00003000
00003100
00003200
00003300
00003400
00003500
00003600
00003700
00003800
00003900
00004000
00004100
00004200
00004300
00004400
00004500
00004600
00004700
00004800
00004900
00005000
00005100
00005200
00005300
00005400
D-12
-------
LENGTH DEFAULT=4;
IF INEPS;
DATA _NULL_;
SET IN4.EPSHDR; * GET REGION COORDINATES;
CALL SYMPUTCXORIGIN',X_ORIGIN);
CALL SYMPUTCYORIGIN' ,Y_ORIGIN) ;
CALL SYMPUT('XMAX',X_MAX);
CALL SYMPUT('YMAX',Y_MAX);
* *
| CALCULATE CANOPY BIOMASS
* *
DATA OUT1.GCPBIO; * CALCULATE THE BIOMASS FOR CANOPY;
MERGE CNPYBF INS.GCNPY(IN=A);
BY COL ROW;
LENGTH DEFAULT=4;
ARRAY VEG(3) OAK DECD CONF;
ARRAY BIOFAC1(4) OAKHI OAKLI OAKNI OAKCF;
ARRAY BIOFAC2(4) DECDHI DECDLI DECDNI DECDCF;
ARRAY BIOFAC3(4) CONFHI CONFLI CONFNI CONFCF;
ARRAY BIOCAT(4) BIOMHI BIOMLI BIOMNI BIOMCF;
* BIOMASS CATEGS. HIGH ISOP, LOW ISOP, NO ISOP, CONF - 8 CANOPY;
ARRAY LAYHI(8) BIOHI1-BIOHI8;
ARRAY LAYLI(8) BIOLI1-BIOLI8;
ARRAY LAYNI(8) BIONI1-BIONI8;
ARRAY LAYCF(8) BIOCF1-BIOCF8;
* BIOMASS LAYER FACTORS FOR DECD & CONF FOREST FROM B.L. CANOPY;
ARRAY LAYDECD(8) LAI1-LAI8;
ARRAY LAYCONF(8) LAI9-LAI16;
IF _N_ - 1 THEN DO;
LAI1-0; LAI2=0; LAI3=0.02; LAI4=0.11; LAI5=0.22;
LAI6-0.35; LAI7=0.22; LAI8=0.09;
LAI9=0.025; LAI10=0.05; LAI11-0.15; LAI12=0.215;
LAI13=0.215; LAI14=0.165; LAI15=0.12; LAI16=0.05;
RETAIN LAI1-LAI16;
END;
IF A;
* WINDOW FOR CURRENT REGION;
IF COL >= &XORIGIN AND COL <= &XMAX AND
ROW >= &YORIGIN AND ROW <= &YMAX;
* IF ALL ZERO THEN DROP THIS COL ROW;
IF OAK <= 0 AND DECD <= 0 AND CONF <= 0 THEN DELETE;
DO J = 1 TO 4; * CALC BIOMASS FOR EMISS CATEGORY & VEG;
BIOCAT(J) - 0;
BIOCAT(J) + SUM(VEG(1) * BIOFACl(J),
VEG(2) * BIOFAC2(J),
VEG(3) * BIOFAC3(J));
END;
DO I = 1 TO 8; * LAYER THE BIOMASS IN CANOPY LAYERS;
LAYHI(I) = BIOCAT(l) * LAYDECD(I);
LAYLI(I) = BIOCAT(2) * LAYDECD(I);
LAYNI(I) - BIOCAT(3) * LAYDECD(I);
LAYCF(I) = BIOCAT(4) * LAYCONF(I);
END;
KEEP COL ROW BIOHI1-BIOHI8 BIOLI1-BIOLI8 BIONI1-BIONI8
00005500
00005600
00005700
00005800
00005900
00006000
00006100
00006200
00006300
00006400
00006500
00006600
00006700
00006800
00006900
00007000
00007100
00007200
00007300
00007400
00007500
00007600
00007700
00007800
00007900
00008000
00008100
00008200
00008300
00008400
00008500
00008600
00008700
00008800
00008900
00009000
00009100
00009200
00009300
00009400
00009500
00009600
00009700
00009800
00009900
00010000
00010100
00010200
00010300
00010400
00010500
00010600
00010700
00010800
D-13
-------
BIOCF1-BIOCF8;
CALCULATE URBAN TREE BIOMASS
DATA OUT2.GUTBIO; * CALCULATE THE BIOMASS FOR CANOPY;
MERGE CNPYBF ING.GUTREE(IN=A);
BY COL ROW;
LENGTH DEFAULT=4;
ARRAY VEG(3) OAK DECD CONF;
ARRAY BIOFAC1(4) OAKHI OAKLI OAKNI OAKCF;
ARRAY BIOFAC2(4) DECDHI DECDLI DECDNI DECDCF;
ARRAY BIOFAC3(4) CONFHI CONFLI CONFNI CONFCF;
ARRAY BIOCAT(4) BIOHI BIOLI BIONI BIOCF;
IF A;
* WINDOW FOR CURRENT REGION;
IF COL >= &XORIGIN AND COL <= &XMAX AND
ROW >= &YORIGIN AND ROW <= &YMAX;
* IF ALL ZERO THEN DROP THIS COL ROW;
IF OAK <= 0 AND DECD <= 0 AND CONF <= 0 THEN DELETE;
DO J = 1 TO 4; * CALC BIOMASS FOR EMISS CATEGORY & VEG;
BIOCAT(J) = 0;
BIOCAT(J) + SUM(VEG(1) * BIOFACl(J),
VEG(2) * BIOFAC2(J),
VEG(3) * BIOFACS(J));
END;
KEEP COL ROW BIOHI BIOLI BIONI BIOCF;
-A- -,y
| CALCULATE NON-CANOPY AREAS
* *
DATA OUT3.GNCBIO; * CALCULATE THE AREAS FOR NON-CANOPY;
MERGE GROWTH IN7.GNCNPY(IN=A);
BY COL ROW;
LENGTH DEFAULT-4;
ARRAY VEG(19) GRASS SCRUB URB_GRSS WATER ALFA BARL CORN COTT
CRP_MS HAY OATS PEANUT POTAT RICE RYE SORG SOYBN TOBAC WHEAT;
IF A;
* WINDOW FOR CURRENT REGION;
IF COL >= &XORIGIN AND COL <= &XMAX AND
ROW >= &YORIGIN AND ROW <= &YMAX;
* IF ALL ZERO THEN DROP THIS COL ROW;
IF GRASS <= 0 AND SCRUB <= 0 AND URB_GRSS <= 0 AND WATER <= 0
AND ALFA <= 0 AND BARL <= 0 AND CORN <= 0 AND COTT <= 0 AND
CRP_MS <= 0 AND
HAY <= 0 AND OATS <= 0 AND PEANUT <= 0 AND POTAT <= 0 AND RICE
<= 0 AND SORG <= 0 AND TOBAC <= 0 AND WHEAT <= 0 THEN DELETE;
DO N = 1 TO DIM(VEG);
VEG(N) = VEG(N) * BIOFAC; * KEEP AREA OR SET TO ZERO;
END;
DROP BIOFAC N;
*;
PROC PRINT DATA-OUTl.GCPBIO(OBS-lOO);
TITLE 'BIOMASS DATA FOR CANOPY VEGETATION';
PROC PRINT DATA=OUT2.GUTBIO(OBS=100);
TITLE 'BIOMASS DATA FOR URBAN TREES';
00010900
00011000
00011100
00011200
00011300
00011400
00011500
00011600
00011700
00011800
00011900
00012000
00012100
00012200
00012300
00012400
00012500
00012600
00012700
00012800
00012900
00013000
00013100
00013200
00013300
00013400
00013500
00013600
00013700
00013800
00013900
00014000
00014100
00014200
00014300
00014400
00014500
00014600
00014700
00014800
00014900
00015000
00015100
00015200
00015300
00015400
00015500
00015600
00015700
00015800
00015900
00016000
00016100
00016200
D-14
-------
PROC PRINT DATA=OUT3.GNCBIO(OBS=100); 00016300
TITLE 'BIOMASS DATA FOR NONCANOPY VEGETATION'; 00016400
*; 00016500
D-15
-------
CORRECT2.SAS
D-16
-------
INI:
IN2:
IN3:
IN4:
OUT1:
OUT2:
GRIDDED HOURLY TEMPERATURE DATA
GRIDDED HOURLY SOLAR RADIATION DATA
GRIDDED HOURLY WIND SPEED DATA
GRIDDED HOURLY RELATIVE HUMIDITY DATA
CANOPY EMISSION CORRECTION FACTORS
NONCANOPY EMISSION CORRECTION FACTORS
*********************#****#***********^
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* RUNSTREAM TO EXECUTE PROGRAM BIO.CORRECT.SAS
*
* CORRECT.SAS USES GRIDDED METEOROLOGICAL DATA TO CALCULATE HOURLY
* CANOPY AND NONCANOPY CORRECTION FACTORS BASED ON TEMP., W.S., SOLAR
* RAD., AND R.H. THE INPUT/OUTPUT FILES ARE AS FOLLOWS:
*
*
*
*
*
*
*
*
*
* NOTE: FOR THIS VERSION, INPUT DATA SETS IN SAS DATA STEPS WERE
* CHANGED TO INFILE.TEMP AND INFILE.RELH TO MATCH THE SAS
* LIBRARY DATA SETS CREATED (THESE SHOULD HAVE BEEN INFILE.BIOTEMP
* AND INFILE.BIORELH). NOTE THAT INFILE.BIOWIND (VERSION 2) AND
* INFILE.BIOSOLR WERE NOT CHANGED AS THESE WERE PROPOERLY CREATED.
* LGM 3/91
*
* OPTIONS SOURCE MPRINT;
* CORRECT.SAS;
PROVIDE METEOROLOGY VALUES FOR THE CANOPY PART OF THE MODEL
AND THEN OUTPUT CANOPY TINGEY CORRECTION VALUES.
* LGM 3/91 - CHANGE INFILE.WIND TO INFILE.BIOWIND FOR VERSION 2;
* LGM 6/90 - CHANGE INFILE.BIOTEMP TO INFILE.TEMP, INFILE.BIOWIND;
* TO INFILE.WIND AND CHANGE INFILE.BIORELH TO INFILE.RELH;
* LGM 6/90 - INCORPORATE NEW NOX EMISSION FACTORS;
IBM/TSO VERSION;
CMS VERSION;
* BRG 5/89 - BIOGENIC EMISSIONS PROCESSING VERSION 2.
* REVISED CANOPY MODEL FOR RADM;
* BRG 3/89 - BIOGENIC EMISSIONS PROCESSING VERSION 2,
* IMPLEMENTATION OF THE CANOPY MODEL;
* BRG 12/88 - BIOGENIC EMISSIONS PROCESSING VERSION 1.0;
LGM 7/89 -
BRG 6/89 -
.1;
.0;
DATA OUT1.CNPEMFAC(KEEP-COL ROW HOUR MONODE1-MONODE8 MONOCF1-MONOCF8
ALPHDE1-ALPHDE8 ALPHCF1-ALPHCF8
ISOPDE1-ISOPDE8 ISOPCF1-ISOPCF8)
*;
*
LENGTH DEFAULT=4;
MERGE IN1.BIOTEMP IN2.BIOSOLR IN3.BIOWIND IN4.BIORELH;
MERGE INI.TEMP IN2.BIOSOLR IN3.BIOWIND IN4.RELH;
BY COL ROW HOUR;
ARRAYS ; 00000381
ARRAY LAI (16) LAI1 - LAI16; 00000382
ARRAY ZI(16)
ZI1-ZI16;
00000383
D-17
-------
ARRAY CAFV(16) CAFV1-CAFV16 ;
ARRAY CAFT(16) CAFT1-CAFT16 ;
ARRAY WADJ ( 8 ) WADJ 1 - WADJ 8 ;
ARRAY CHIGHT(2) CHIGHT1-CHIGHT2 ;
* HOURLY EMISSION CORRECTION FACTORS - FOR BIOMASS TYPE - 8 LAYERS;
ARRAY MONO(16) MONODE1-MONODE8 MONOCF1-MONOCF8 ;
ARRAY ALPH(16) ALPHDE1-ALPHDE8 ALPHCF1-ALPHCF8 ;
ARRAY ISOP(16) ISOPDE1-ISOPDE8 ISOPCF1-ISOPCF8 ;
*
* .... PROGRAM VARIABLES
*;
IF N - 1 THEN DO;
LAI1=0; LAI2-=0; LAI3=0.02; LAI 4=0. 11;
LAI5=0.22; LAI6=0.35; LAI7=0.22; LAI8=0.09;
LAI9-0.025; LAI10=0.05; LAI11-0.15; LAI12=0.215;
LAI13-0.215; LAI14=0.165; LAI15-0.12; LAI16=0.05;
CAFT1=0.339; CAFT2=0.339; CAFT3=0.339; CAFT4=0.346;
CAFT5=0.389; CAFT6=0.493; CAFT7=0.717; CAFT8=0.908;
CAFT9=0.195; CAFT10=0 . 204 ; CAFT11=0. 221 ; CAFT12=0 . 283 ;
CAFT13=0.404; CAFT14=0 . 575 ; CAFT15=0. 755 ; CAFTl6=0 . 921 ;
CAFV1-0.0799; CAFV2=0.0799 ; CAFV3=0.0799 ; CAFV4=0 . 0840 ;
CAFV5=0.1106; CAFV6=0. 1918 ; CAFV7=0.4604 ; CAFV8=0. 7984 ;
CAFV9-0.0221; CAFV10=0.0244; CAFV11=0. 0295 ; CAFV12=0 . 0526 ;
CAFV13=0.1204; CAFV14=0 . 2754; CAFV15=0. 5198 ; CAFV16=0. 8249 ;
ZI1-3.75; ZI2=5.25; ZI3=6.75; ZI4=8.25;
ZI5=9.75; ZI6=11.25; ZI7=12.75; ZI8=14.25;
ZI9=5.0; ZI10-7.0; ZI11-9.0; ZI12=11.0;
ZI13-12.0; ZI14-15.0; ZI15-17.0; ZI16-19.0;
WADJ 1-1.0; WADJ2-1.0; WADJ3-1.0; WADJ4=1.0;
WADJ 5-1.0; WADJ 6=0. 1846; WADJ7=0. 6846 ; WADJ8=0.917;
Kl=0.0162; K2=0.026; LLENTH=10.0;
CHIGHT1=15; CHIGHT2=20;
*;
RETAIN LAI1-LAI16 CAFT1-CAFT16 ZI1-ZI16 WADJ1-WADJ8 Kl K2 LLENTH
CHIGHT1-CHIGHT2 CAFV1-CAFV16 ;
END;
*!
TAIR = TEMP + 273;
* 8.132E-11 = STEFAN BOL. CONSTANT;
GROUNDO = 8.132E-11*((TAIR)**4);
*;
TRATE = 0.06;
IF SOLARC <= 0.0 THEN TRATE = -0.06;
*;
* SET LOWER LAYERS TO ZERO FOR DECID - NO BIOMASS THERE;
MONODE1 =0.0;
MONODE2 =0.0;
ISOPDE1 - 0.0;
ISOPDE2 =0.0;
ALPHDE1 =0.0;
ALPHDE2 =0.0;
*;
DO CNTYPE = 0 TO 1;
*;
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D-18
-------
*.
*;
*.
*:
*;
*
*,
*
*
*.
*;
*.
*.
*.
*;
*.
*
*
*;
*.
*.
*;
*
NO TLEAF CALCULATIONS FOR THESE CNTYPES: USE TEMPERATURE VALUES..; 00000438
00000439
DO I - 8 TO 1 BY -1; 00000440
BIOMASS ; 00000441
00000442
IF LAI(CNTYPE*8+I) <= 0 THEN GO TO OUTLOOP2; 00000443
TLEAFAA=0.0; 00000444
SOLAR=0.0; 00000445
00000446
..OUTLOOP FOR ZERO VERTICAL BIOMASS TO SAVE COMPUTER TIME USAGE ; 00000447
. .CONVERSION FROM G BIOM/M2 GROUND TO KGRAM BIOM/M2 TO M2 LEAF AREA/M200000448
GROUND LINDE1 BACK TO CM2 L A/M2 UNDERSTORY DISTRIBUTION NOT 00000449
ACCOUNTED FOR IN VERTICAL ; 00000450
. .FINAL UNITS *** GRAMS BIOMASS/VERTICAL/M**2 GROUND ;00000451
00000452
SOLARZ ; 00000453
.. K = 0.42 IS AN ESTIMATED EXTINCTION COEFF FOR VISIBLE SPECTRUM; 00000454
. . K = 0.18 IS AN ESTIMATED EXTINCTION COEFF FOR THE TOTAL SPECTRUM; 00000455
00000456
00000457
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00000459
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00000461
00000462
00000463
00000464
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TEMPZ: FROM TOP DOWN ( ) ; 00000467
TCHANG = TRATE*(CHIGHT(CNTYPE+1) - ZI(CNTYPE*8+I));
TEMPC = TAIR - TCHANG;
ADD IN IR FROM GROUND PLUS 10% REFLECTANCE BACK UP ;
ASSUME IR ATTENUATED INVERSELY TO CANOPY HT. BY BIOMASS
GROUND
SOLAN =
SOLAR =
= GROUNDO*(1-CAFT(CNTYPE*8+I));
SOLARC * (1.1 * CAFT(CNTYPE*8+1)) + GROUND;
1.1*CAFV(CNTYPE*8+I)*SOLARE;
. .LINDEX=M2 LEAF AREA(ONE SIDE ONLY)/M2 GROUND, SUMMED FROM TOP DOWN;
..FINAL UNITS *** CALORIES/CM**2-MIN ;
*;
*.
*;
*.
*.
*
FINAL UNITS *** DEGREES KELVIN
SVP1 = 4.9283*LOG10(TEMPC);
SVP - 10**((-2937.4/TEMPC)-SVPl+23.5518);
*;
*.
*
*.
*;
*.
*
00000468
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HUMIDITY ; 00000473
SATURATION VAPOR PRESSURE (MILLIBARS) ; 00000474
00000475
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WINDSZ ; 00000490
CALCULATION IN TWO PARTS: UPPER CANOPY WIND IS LOGARITHMIC ; 00000491
IF TEMPC >
DELTAH
ELSE
DELTAH >
283 THEN
7.0;
1-5;
RELATIVE HUMIDITY FROM HOURLY FILES ;
HUMID = (RELH*SVP) + (DELTAH/CHIGHT(CNTYPE+1))*
(CHIGHT(CNTYPE+1) - ZI(CNTYPE*8+I))
FINAL UNITS *** MILLIBARS ;
D-19
-------
*. . .
*. . .
*. . .
*. . .
*. . .
*
1
*. . .
*. . .
*. . .
*
*;
*;
*. . .
*
* ...
*. . .
* ;
*. . .
*;
*;
* ;
*;
*;
*;
*. . .
*. . .
*. . .
*;
*. . .
LOWER CANOPY IS LINEAR ;
CALCULATION OF HEIGHT WHERE WINDSPEED ENDS LOG DIST. ;
IE. GOES BELOW 0.1 M/SEC, IS ASSUMED TO BE 0.1M/SEC FOR THE REST ;
LOWER CANOPY VALUES FOR WINDSPEED: ASSIGNED ARBITRARY DUE ;
LACK OF UNDERSTORY DATA ;
IF I < 6 THEN WINDC -0.1;
UPPER CANOPY LOGARITHMIC DETERMINATION OF WINDSPEED ;
LOGARITHMIC WIND FORMULA ;
ELSE DO;
WINDC - WIND * WADJ(I);
IF WINDC < 0 . 1 THEN WINDC =0.1;
END;
FINAL UNITS *** METERS/SEC ;
LEAF SIZES ****(CM) ;
LWIDTH = 5.0;
IF CNTYPE - 1 THEN LWIDTH =1.0;
REVERSAL OF LEAF ORIENTATION WITH RESPECT TO WIND ;
TLEAFZ ;
NEWTON BISECTIONAL METHOD OF SOLVING EQUATION ;
A) PRECALCULATIONS/CONVERSIONS (T CON. IN PROGRAM) ;
WINDSCM = WINDC*100;
LEAF RESISTANCE IN MIN/CM;
LRESIS = (0.03233 / ((0.01 + SOLAN - GROUND) **0. 99) ) + 0.025;
VAPOR1 - 0.0002165 *SVP/TEMPC;
RELHUM1 = HUMID/SVP;
IF RELHUM1 > 1.0 THEN RELHUM1 -1.0;
L2 - LRESIS + (K2*
(LWIDTH**0 . 2*LLENTH**0 . 35/WINDSCM**0 . 55) ) ;
Q = SOLAN * 0.5;
TLEAFAA = TEMPC ; LEFTA =TEMPC + 20;
RIGHTA = TEMPC - 20;
CONTIN: ZERO = 0;
DO N = 1 TO 3 BY 1;
IF N = 1 THEN TLEAFA - LEFTA;
IF N = 2 THEN TLEAFA = RIGHTA;
IF N = 3 THEN TLEAFA - TLEAFAA;
VAPOR DENSITY FOR LEAF TEMPS ;
SATURATION VAPOR DENSITIES (G/CM*3) ;
SATURATION VAPOR PRESSURE (MILLIBARS) ;
SVP1 = 4. 9283*LOG10 (TLEAFA);
SVP - 10**((- 2937. 4/TLEAFA)-SVPl+23. 5518);
SATURATION VAPOR DENSITY (GRAMS/CM3) ;
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D-20
-------
*.
*
*;
*.
*
*.
*
*;
*
*
*
VAPOR2 - 0.0002165*SVP/TLEAFA;
. LATENT HEAT OF VAPORIZATION (CAL/GRAM H20 ;
LHV = 597 - (0.57*(TLEAFA - 273));
LI = (VAPOR2 - RELHUM1*VAPOR1);
L3 = LHV*(L1/L2);
. IR RADIATION BY LEAF, ATTENUATED BY CANOPY ;
RI1 - 0.95*8.132E-11*(TLEAFA)**4;
Rl = RI1*(1-CAFT(CNTYPE*8+I));
Cl = K1*((WINDSCM/LLENTH)**0.5)*(TLEAFA TEMPC);
. TOTAL RADIATION ABSORBED BY LEAF=HALF AVAILABLE ;
SUBTOT = Q - Rl - Cl - L3;
IF N = 1 THEN LEFT = SUBTOT;
IF N = 2 THEN RIGHT = SUBTOT;
IF N = 3 THEN DO;
PROD = SUBTOT*RIGHT;
IF PROD < 0.0 THEN DO;
LEFTA = TLEAFAA;
TLEAFAA = (LEFTA + RIGHTA)*0.5;
END;
IF PROD > 0.0 THEN DO;
RIGHTA = TLEAFAA;
TLEAFAA = (LEFTA + RIGHTA)*0.5;
END;
IF (LEFTA - RIGHTA) > 0.01 THEN GO TO CONTIN;
IF (LEFTA - RIGHTA) > 1.0 THEN GO TO CONTIN;
IF (LEFTA - RIGHTA) > 0.5 THEN GO TO CONTIN;
END;
END; *END OF N LOOP;
*... E) END/FINAL CALCULATION ;
* CONVERT TLEAF BACK TO CELCIUS AND LOAD INTO THE ARRAYS;
TLEAFAA - TLEAFAA - 273;
*;
* USE MODIFIED TINGEY CURVES - PRESENTED HERE AS CORRECTION FACTORS;
* R FACTORS ARE TO CONVERT THE EMISSION FACTORS AT FULL SUNLIGHT TO A;
* LOWER LIGHT INTENSITY: R400 =1.95 R200 =4.75 R100 = 10.72;
MONO(CNTYPE*8+I) = EXP(0.0739 * (TLEAFAA-30));
ALPH(CNTYPE*8+I) - EXP(0.067 * (TLEAFAA-30));
* PICK APPRO ISOP CURVE - INTERPOLATE IF BETWEEN CURVES;
IF SOLAR >- 800 THEN
ISOP(CNTYPE*8+I)=10**(1.200/(1+EXP(-0.400*(TLEAFAA-28.30)))-0.796)
ELSE IF SOLAR < 800 AND SOLAR > 400 THEN DO;
F800 -= 10**(1.200/(1+EXP(-0.400*(TLEAFAA-28.30)))-0.796);
F400 - 10**(0.916/(l+EXP(-0.239*(TLEAFAA-29.93)))-0.462);
ISOP(CNTYPE*8+I) = F400/1.95 + (F800-F400/1.95) * (SOLAR-400)/400
END;
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; 00000594
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;00000598
00000599
D-21
-------
ELSE IF SOLAR = 400 THEN DO;
F400 = 10**(0.916/(l+EXP(-0.239*(TLEAFAA-29.
ISOP(CNTYPE*8+I) <= F400/1.95;
END;
ELSE IF SOLAR < 400 AND SOLAR > 200 THEN DO;
F400 - 10**(0.916/(l+EXP(-0.239*(TLEAFAA-29.
F200 -= 10**(0.615/(l+EXP(-0.696*(TLEAFAA-32.
ISOP(CNTYPE*8+I) = F200/4.75 + (F400/1.95-F200/4.75)
END;
ELSE IF SOLAR - 200 THEN DO;
F200 = 10**(0.615/(l+EXP(-0.696*(TLEAFAA-32.
ISOP(CNTYPE*8+I) = F200/4.75;
END;
ELSE IF SOLAR < 200 AND SOLAR > 100 THEN DO;
F200 - 10**(0.615/(l+EXP(-0.696*(TLEAFAA-32.
F100 = 10**(0.437/(1+EXP(-0.312*(TLEAFAA-31.
ISOP(CNTYPE*8+I) = F100/10.73+(F200/4.75-F100/10.73)
END;
ELSE IF SOLAR = 100 THEN DO;
FIDO = 10**(0.437/(1+EXP(-0.312*(TLEAFAA-31.
ISOP(CNTYPE*8+I) = F100/10.73;
END;
ELSE IF SOLAR < 100 AND SOLAR > 0 THEN DO;
FIDO = 10**(0.437/(1+EXP(-0.312*(TLEAFAA-31.
ISOP(CNTYPE*8+I) = (F100/10.73) * SOLAR/100;
END;
ELSE IF SOLAR <= 0 THEN
ISOP(CNTYPE*8+I) = 0;
93)))-0.462);
93)))-0.462);
79)))-0.077);
* (SOLAR-200)/200;
79)))-0.077);
79)))-0.077);
75)))-0.160);
* (SOLAR-100)/100;
75)))-0.160);
75)))-0.160);
OUTLOOP2:
*;
END;
*;
END;
*
*END OF I LOOP - CANOPY LAYERS 8-1;
*END OF TYPE LOOP;
CALCULATE NON-CANOPY EMISSION CORRECTION FACTORS USING TINGEY |
CURVES.
USE MODIFIED TINGEY CURVES - PRESENTED HERE AS CORRECTION FACTORS
R FACTORS ARE TO CONVERT THE EMISSION FACTORS AT FULL SUNLIGHT TO A
LOWER LIGHT INTENSITY: R400 =1.95 R200 =4.75 R100 - 10.72
USE AIR TEMPERATURE TO DETERMINE SOIL TEMPERATURE AND A NO EMISSION
FLUX FOR SOIL NO.
DATA OUT2.NCEMFAC;
*MERGE IN1.BIOTEMP IN2.BIOSOLR;
MERGE INI.TEMP IN2.BIOSOLR;
BY COL ROW HOUR;
LENGTH DEFAULT=4;
* CALCULATE CORRECTION FACTORS FOR EACH SPECIE, EACH HOUR;
MONO = EXP(0.0739 * (TEMP-30));
ALPH = EXP(0.067 * (TEMP-30));
* PICK APPRO ISOP CURVE - INTERPOLATE IF BETWEEN CURVES;
IF SOLARE >= 800 THEN
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;00000644
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D-22
-------
ISOP = 10**(1.200/(1+EXP(-0.400*(TEMP-28.30)))-0.796); 00000655
ELSE IF SOLARE < 800 AND SOLARE > 400 THEN DO; 0-0000656
F800 - 10**(1.200/(1+EXP(-0.400*(TEMP-28.30)))-0.796); 00000657
F400 -= 10**(0.916/(l+EXP(-0.239*(TEMP-29.93)))-0.462); 00000658
ISOP - F400/1.95 + (F800 - F400/1.95) * (SOLARE - 400) / 400; 00000659
END; 00000660
ELSE IF SOLARE - 400 THEN DO; 00000661
F400 - 10**(0.916/(l+EXP(-0.239*(TEMP-29.93)))-0.462); 00000662
ISOP - F400/1.95; 00000663
END; 00000664
ELSE IF SOLARE < 400 AND SOLARE > 200 THEN DO; 00000665
F400 = 10**(0.916/(l+EXP(-0.239*(TEMP-29.93)))-0.462); 00000666
F200 - 10**(0.615/(l+EXP(-0.696*(TEMP-32.79)))-0.077); 00000667
ISOP - F200/4.75 + (F400/1.95 - F200/4.75) * (SOLARE - 200) / 200; 00000668
END; 00000669
ELSE IF SOLARE - 200 THEN DO; 00000670
F200 = 10**(0.615/(l+EXP(-0.696*(TEMP-32.79)))-0.077); 00000671
ISOP = F200/4.75; 00000672
END; 00000673
ELSE IF SOLARE < 200 AND SOLARE > 100 THEN DO; 00000674
F200 = 10**(0.615/(l+EXP(-0.696*(TEMP-32.79)))-0.077); 00000675
FIDO = 10**(0.437/(1+EXP(-0.312*(TEMP-31.75)))-0.160); 00000676
ISOP = F100/10.73 + (F200/4.75 - F100/10.73) * (SOLARE - 100) / 100; 00000677
END; 00000678
ELSE IF SOLARE = 100 THEN DO; 00000679
F100 - 10**(0.437/(1+EXP(-0.312*(TEMP-31.75)))-0.160); 00000680
ISOP -= F100/10.73; 00000681
END; 00000682
ELSE IF SOLARE < 100 AND SOLARE > 0 THEN DO; 00000683
FIDO - 10**(0.437/(1+EXP(-0.312*(TEMP-31.75)))-0.160); 00000684
ISOP - (F100/10.73) * SOLARE / 100; 00000685
END; 00000686
ELSE IF SOLARE <= 0 THEN 00000687
ISOP = 0; 00000688
*; 00000689
***** NEW NO ALGORITHM ADDED 6/89 LGM *******************; 00000690
*; 00000691
* THE ORIGINAL NOX CALCULATIONS (BELOW) HAVE BEEN SUPERCEDED BY THE; 00000692
* FOLLOWING ALGORITHMS WHICH WERE TAKEN FROM A FAX SENT TO MARK; 00000693
* SAEGER FROM FRED FEHSENFELD (NOAA) ON MAR 21, 1989.; 00000694
* NOTE THE FOLLOWING UNITS: TEMPERATURE: DEGREES C; 00000695
* NO FLUX : NG NITROGEN/SQ M * SECONDS; 00000696
*; 00000697
STEMP - (0.70*TEMP) +3.6; 00000698
QNO - 0.74*EXP(0.079*STEMP); 00000699
*; 00000700
************** OLD NO ALGORTHM COMMENTED OUT 6/89 LGM ********; 00000701
*. 00000702
*'CALCULATE HOURLY NOX FLUX FOR EACH GRID CELL; 00000703
* DETAILED EQUATION: STEMP KELVIN - (0.69 * TEMP CELSIUS + 2.1) + 273; 00000704
* DETAILRD EQUATION: QNO NG N/M2/SEC - 5.1E16 NG N/M2/SEC/PPM * 2 PPM 00000705
* EXP(-97000 J MOLE /(8.314 J/MOLE/KELVIN * STEMP KELVIN)); 00000706
*STEMP - 0.69 * TEMP + 275.1; 00000707
*QNO - 10.2E16 * EXP(-97000/(8.314 * STEMP)); 00000708
D-23
-------
KEEP COL ROW HOUR ISOP MONO ALPH QNO; 00000709
* UNITS FOR QNO ARE NG N/M2/SEC; 00000710
*; 00000712
*PROC PRINT DATA=OUT1.CNPEMFAC(OBS=500); 00000713
*TITLE 'CANOPY EMISSION CORRECTION FACTORS - FIRST 500 DBS'; 00000714
*PROC PRINT DATA=OUT2.NCEMFAC(OBS=500); 00000715
*TITLE 'NONCANOPY EMISSION CORRECTION FACTORS - FIRTST 500 DBS'; 00000716
* 00000720
D-24
-------
RADMBIO.SAS
D-25
-------
******************************************************************
* RUNSTREAM TO EXECUTE PROGRAM BIO. RADMBIO.SAS
k
* RADMBIO.SAS CALCULATES STANDARD EMISSIONS AS A FUNCTION OF BIOMASS
* AND THEN APPLIES HOURLY TEMPERATURE AND SOLAR RADIATION CORRECTION
* FACTORS FROM CORRECT2 . SAS . THE INPUT/OUTPUT FILES ARE:
k
* INI: GRIDDED NONCANOPY LAND COVERAGE (FROM BIOMASS. SAS)
* IN2: GRIDDED BIOMASS FOR URBAN TREES (FROM BIOMASS. SAS)
* IN3: NONCANOPY EMISSION CORRECTION FACTORS (FROM CORRECT2 . SAS )
* IN4: GRIDDED CANOPY BIOMASS (FROM BIOMASS. SAS)
* INS: CANOPY EMISSION CORRECTION FACTORS (FROM CORRECT2 . SAS )
*
* INTER!: ADJUSTED GRIDDED HOURLY NONCANOPY EMISSIONS
* INTER2: ADJUSTED GRIDDED HOURLY CANOPY EMISSIONS
*
OUT1: COMBINED CANOPY AND NONCANOPY BIOGENIC EMISSIONS FOR
RADM INPUT
*****************************************************************
* RADMBIO.SAS;
READS IN THE CANOPY AND NON -CANOPY BIOFACTORS AND THE GRIDDED
BIOMASS AND CREATES THE DATASET FOR MERGING WITH THE OTHER
EMISSIONS SOURCES (MAJOR, MINOR, AREA)
iV- .
* LGM 7/89 - IBM/TSO VERSION;
* BRG 6/89 CMS VERSION;
* BRG 5/89 - BIOGENIC EMISSIONS PROCESSING VERSION 2.1;
k REVISED CANOPY MODEL FOR RADM;
* BRG 3/89 - BIOGENIC EMISSIONS PROCESSING VERSION 2.0;
* IMPLEMENTATION OF THE CANOPY MODEL;
* BRG 2/89 BIOGENIC EMISSIONS PROCESSING VERSION 2.0;
* ADD SOIL NO AND N02 - CONVERT FROM MOLE/SEC TO G/SEC;
* BRG 12/88 - BIOGENIC EMISSIONS PROCESSING VERSION 1.0;
k
V PAT PUT ATF FMT^TOW? FDR PATH rRTD TFT T K, HOTTR
NON- CANOPY VERSION
WATER DATA HAS BEEN DELETED FOR THE 2.1 BEIS.
WAT ER WAT1-WAT4 WAT ER = 145.0 WAT1 = 0 WAT2 = 0 WAT3 = 0
WAT4 =1.00 WAT_ER WAT1-WAT4 WATER
k . ... ......
* ;
* SET UP EMISSION RATES & PERCENT COMPOSITIONS;
DATA EMISRATE;
LENGTH DEFAULT=4;
ARRAY EMISRTE(IS) ALF ER SOR ER HAY ER SOY ER COR ER POT ER TOB ER
WHT_ER COT_ER RYE_ER RIC_ER PEA_ER BAR_ER OAT_ER RNG ER
GRS_ER UGR ER CMS ER;
* COMPOSITION TYPES ARE ISOP, ALPHA, MONO, UNKNOW;
ARRAY EMISCMP(72) ALF1-ALF4 SOR1-SOR4 HAY1-HAY4 SOY1-SOY4 COR1-COR4
POT1-POT4 TOB1-TOB4 WHT1-WHT4 COT1-COT4 RYE1-RYE4 RIC1-RIC4
00000220
00000230
00000240
00000250
00000260
00000270
00000280
00000290
00000300
00000310
00000320
00000325
00000330
00000340
00000350
00000360
00000370
00000385
00000392
&nnr\nn*^QA
00000395
00000396
00000397
* 00000^98
) \J\S\J\J\Jjy\J
00000399
00000400
00000401
00000402
00000403
00000404
00000405
00000406
00000407
00000408
00000409
& nnnnnii n
" \J\_/wUUHlv
00000411
00000412
00000413
00000414
00000415
* fifinnn/. i c.
" , UUUUUHj.0
00000417
00000418
00000419
00000420
00000421
00000422
00000423
00000424
00000425
00000426
D-26
-------
ALF
SOR~
HAY~
SOY~
COR~
POT"
TOB"
WHT
COT"
RYE"
RIC~
PEA"
BAR"
OAT
RNG
GRS
UGR
CMS
ALF1
SOR1
HAY1
SOY1
COR1
POT1
TOB1
WHT1
COT1
RYE1
RIC1
PEA1
BAR1
OAT1
RNG1
GRS1
UGR1
CMS1
PEA1-PEA4 BAR1-BAR4 OAT1-OAT4 RNG1-RNG4
UGR1-UGR4 CMS1-CMS4;
ER - 37.9;
ER - 39.4;
ER = 189.0;
ER - 22.2;
ER - 3542.0;
ER = 48.1;
ER = 294.0;
ER = 30.0;
ER - 37.9;
ER - 37.9;
ER - 510.0;
ER - 510.0;
ER - 37.9;
ER - 37.9;
ER = 189.0;
ER
ER
ER
=
=
=
GRS1-GRS4
= 281.0;
= 281.0;
= 37
.50
.20
.20
)
1
t
= 1.00
=
-
=
=
=
=
>=
=
=
=
=
=
«=
=
0;
0;
0;
.50
.20
.20
.20
.20
.20
.20
.20
.20
.20
.20
t
1
*
f
t
1
I
)
t
1
J
.9;
ALF2 = .
SOR2 = .
HAY2 = .
; SOY2 =
COR2 - .
POT2 = .
TOB2 = .
WHT2 = .
COT2 = .
RYE2 - .
RIC2 = .
PEA2 = .
BAR2 = .
OAT2 = .
RNG2 = .
GRS2 = .
UGR2 = .
CMS2 = .
10
25
25
0;
10
25
10
10
25
25
25
25
25
25
25
25
25
25
*
*
j
i
»
i
j
J
t
t
i
t
t
1
j
!
1
J
1
URBAN
CROPS
ALF3 =
SOR3 =
HAY3 =
SOY3 =
COR3 =
POT3 =
TOB3 =
WHT3 =
COT3 =
RYE 3 =
RIC3 =
PEA3 =
BAR3 =
OAT 3 =
RNG3 =
GRS 3 =
UGR3 =
CMS 3 =
GRASS
MISC;
.10;
.25;
.25;
0;
.10;
.25;
.10;
.10;
.25;
-25;
.25;
.25;
-25;
.25;
.25;
.25;
.25;
.25;
i
ALF4 =
SOR4 =
HAY4 =
SOY4 =
COR4 =
POT4 =
TOB4 =
WHT4 =
COT4 =
RYE4 =
RIC4 -
PEA4 =
BAR4 =
OAT4 =
RNG4 =
GRS4 =
UGR4 -
CMS4 -
.30
.30
.30
0;
.80
.50
.80
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
.30
)
j
!
J
)
;
»
1
»
J
)
J
)
I
J
J
}
DATA EMISA;
SET IN1.GNCBIO;
LENGTH DEFAULT-4;
IF (_N_ - 1) THEN DO;
SET EMISRATE; *BRING IN EMISSION RATES & COMPOSITIONS;
* CONVERT FACTOR TO GET EMISSIONS INTO G/HR (HA TO M2 AND UG TO G) ;
CONVR - IE-2;
END;
RETAIN CONVR;
* EMISSION RATES BY VEG TYPE;
ARRAY EMISRTE(18) ALF_ER SOR_ER HAY_ER SOY_ER COR_ER POT_ER TOB_ER
WHT_ER COT_ER RYE_ER RIC_ER PEA_ER BAR_ER OAT_ER RNG_ER
GRS_ER UGR_ER CMS_ER;
* COMPOSITION TYPES ARE ISOP, ALPHA, MONO, UNKNOW;
ARRAY EMISCMP(72) ALF1-ALF4 SOR1-SOR4 HAY1-HAY4 SOY1-SOY4 COR1-COR4
00000427
00000428
00000429
00000430
00000431
00000432
00000433
00000434
00000435
00000436
00000437
00000438
00000439
00000440
00000441
00000442
00000443
00000444
00000445
00000446
00000447
00000448
00000449
00000450
00000451
00000452
00000453
00000454
00000455
00000456
00000457
00000458
00000459
00000460
00000461
00000462
00000463
00000464
00000465
00000466
00000467
00000468
00000469
00000470
00000471
00000472
00000473
00000474
00000475
00000476
00000477
00000478
00000479
00000480
D-27
-------
POT1-POT4 TOB1-TOB4 WHT1-WHT4 COT1-COT4 RYE1-RYE4 RIC1-RIC4
PEA1-PEA4 BAR1-BAR4 OAT1-OAT4 RNG1-RNG4 GRS1-GRS4
UGR1-UGR4 CMS1-CMS4;
* BIOMASS TYPES;
ARRAY VEG(18) ALFA SORG HAY SOYBN CORN POTAT TOBAC WHEAT COTT RYE RICE
PEANUT BARL OATS SCRUB GRASS URB_GRSS CRP_MS;
* EMISSION AMOUNTS AISAMT AMOAMT AALAMT AUNAMT;
AISAMT - 0;
AMOAMT - 0;
AALAMT - 0;
AUNAMT -= 0;
DO I - 1 TO DIM(VEG);
AMT - VEG(I) * EMISRTE(I) * CONVR; * CALC EMISSION RATE;
J - (I - 1) * 4; * CALCULATE OFFSET;
AISAMT + (EMISCMP(J+1) * AMT);
AMOAMT + (EMISCMP(J+2) * AMT);
AALAMT + (EMISCMP(J+3) * AMT);
AUNAMT + (EMISCMP(J+4) * AMT);
END;
NOXGRS - SUM(URB_GRSS,GRASS) * 1E4; *KEEP GRASS AREA IN M2 FOR NOX;
KEEP COL ROW AISAMT AMOAMT AALAMT AUNAMT NOXGRS;
* *
I URBAN TREES I
DATA EMISRATE;
LENGTH DEFAULT=4;
ARRAY ISOPER(4) ISOPER1 - ISOPER4;
ARRAY ALPHER(4) ALPHER1 - ALPHER4;
ARRAY MONOER(4) MONOER1 - MONOER4;
ARRAY UNKWER(4) UNKWER1 - UNKWER4;
* EMISSION TYPES ARE HIGH ISOP, LOW ISOP, NON-IOSP, CONF;
ISOPER1 = 14.69;
ISOPER2 - 6.60;
ISOPER3 =0.0;
ISOPER4 =0.0;
ALPHER1 - 0.13;
ALPHER2 = 0.05;
ALPHER3 = 0.07;
ALPHER4 - 1.13;
MONOER1 - 0.11;
MONOER2 = 0.05;
MONOER3 = 0.07;
MONOER4 = 1.29;
UNKWER1 - 3.24;
UNKWER2 - 1.76;
UNKWER3 = 1.91;
UNKWER4 = 1.38;
DATA EMISB; * CALCULATE EFFECTIVE EMISSION RATE FOR EACH COMPOUND;
SET IN2.GUTBIO;
LENGTH DEFAULT=4;
IF (_N_ - 1) THEN DO;
SET EMISRATE; *BRING IN EMISSION RATES;
* CONVERT FACTOR TO GET EMISSIONS INTO G/HR - KG TO G AND UG TO G;
CONVR - 10**-3;
00000481
00000482
00000483
00000484
00000485
00000486
00000487
00000488
00000489
00000490
00000491
00000492
00000493
00000494
00000495
00000496
00000497
00000498
00000499
00000500
00000501
00000502
00000503
00000504
00000505
00000506
00000507
00000508
00000509
00000510
00000511
00000512
00000513
00000514
00000515
00000516
00000517
00000518
00000519
00000520
00000521
00000522
00000523
00000524
00000525
00000526
00000527
00000528
00000529
00000530
00000531
00000532
00000533
00000534
D-28
-------
END;
RETAIN CONVR;
* EMISSION TYPES ARE HIGH ISOP, LOW ISOP, NON-IOSP, CONF;
ARRAY ISOPER(4) ISOPER1 - ISOPER4;
ARRAY ALPHER(4) ALPHER1 - ALPHER4;
ARRAY MONGER(4) MONOER1 - MONOER4;
ARRAY UNKWER(4) UNKWER1 - UNKWER4;
* BIOMASS AMOUNTS TYPES ARE HIGH ISOP, LOW ISOP, NON-ISOP, CONF;
ARRAY BIOMASS(4) BIOHI BIOLI BIONI BIOCF;
BISAMT - 0;
BMOAMT - 0;
BALAMT - 0;
BUNAMT - 0;
DO I - 1 TO 4;
BISAMT + (BIOMASS(I) * ISOPER(I) * CONVR); *CALC EMISSION RATE
BMOAMT + (BIOMASS(I) * MONOER(I) * CONVR);
BALAMT + (BIOMASS(I) * ALPHER(I) * CONVR);
BUNAMT + (BIOMASS(I) * UNKWER(I) * CONVR);
END;
KEEP COL ROW BISAMT BMOAMT BALAMT BUNAMT;
* . .
| COMBINE URBAN TREES AND OTHER NON-CANOPY VEGATION
*
DATA INTER1.GNCEM;
MERGE EMISA IN3.NCEMFAC EMISB;
BY COL ROW;
LENGTH DEFAULT=4;
* HYDROCARBON UNITS ARE G/SEC - ADJUST HOURLY;
NCISOP - ISOP * SUM(AISAMT, BISAMT);
NCMONO - MONO * SUM(AMOAMT, BMOAMT);
NCALPH - ALPH * SUM(AALAMT, BALAMT);
NCUNKW - MONO * SUM(AUNAMT, BUNAMT);
* GET HOURLY SOIL NO AND N02 IN MOLE/SEC;
* DETAILED EQUATIONS: NO NG N/SEC = GNO NG N/M2/SEC * NOXGRS M2;
* NCNO MOLE/SEC = NO NG N/SEC * 1G/1E9NG * 1MOLE/14.0067G;
* NCN02 MOLE/SEC = NCNO MOLE/SEC * 0.06; *N02 IS 6% OF NO;
NCNO = (QNO * NOXGRS) * 7.13944E-11;
IF NCNO = . THEN NCNO =0.0;
NCN02 = NCNO * 0.06;
KEEP COL ROW HOUR NCISOP NCMONO NCALPH NCUNKW NCNO NCN02;
*;
*' CALCULATE EMISSIONS FOR EACH GRID CELL & HOUR
I CANOPY VERSION
DATA EMISRATE;
LENGTH DEFAULT-4;
ARRAY ISOPER(4) ISOPER1
ARRAY ALPHER(4) ALPHER1
ARRAY MONGER(4) MONOER1
ISOPER4;
ALPHER4;
MONOER4;
UNKWER4;
LOW
ARRAY UNKWER(4) UNKWER1
* EMISSION TYPES ARE HIGH ISOP,
ISOPER1 - 14.69;
ISOPER2 - 6.60;
ISOPER3 - 0.0;
ISOP, NON-IOSP, CONF;
00000535
00000536
00000537
00000538
00000539
00000540
00000541
00000542
00000543
00000544
00000545
00000546
00000547
00000548
00000549
00000550
00000551
00000552
00000553
00000554
* 00000555
00000556
*;00000557
00000558
00000559
00000560
00000561
00000562
00000563
00000564
00000565
00000566
00000567
00000568
00000569
00000570
00000571
00000572
00000573
00000574
00000575
-* 00000576
| 00000577
-*;00000578
00000579
00000580
00000581
00000582
00000583
00000584
00000585
00000586
00000587
00000588
D-29
-------
ISOPER4 - 0.0; 00000589
ALPHER1 =0.13; 00000590
ALPHER2 - 0.05; 00000591
ALPHER3 - 0.07; 00000592
ALPHER4 - 1.13; 00000593
MONOER1 - 0.11; 00000594
MONOER2 - 0.05; 00000595
MONOER3 - 0.07; 00000596
MONOER4 = 1.29; 00000597
UNKWER1 - 3.24; 00000598
UNKWER2 - 1.76; 00000599
UNKWER3 = 1.91; 00000600
UNKWER4 = 1.38; 00000601
DATA CNPEMIS; * CALCULATE EFFECTIVE EMISSION RATE FOR EACH COMPOUND; 00000602
SET IN4.GCPBIO; 00000603
LENGTH DEFAULT=4; 00000604
IF (_N_ = 1) THEN DO; 00000605
SET EMISRATE; *BRING IN EMISSION RATES; 00000606
* CONVERT FACTOR TO GET EMISSIONS INTO G/HR = KG TO G AND UG TO G; 00000607
CONVR = 10**-3; 00000608
END; 00000609
RETAIN CONVR; 00000610
* EMISSION TYPES ARE HIGH ISOP, LOW ISOP, NON-IOSP, CONF; 00000611
ARRAY ISOPER(4) ISOPER1 ISOPER4; 00000612
ARRAY ALPHER(4) ALPHER1 - ALPHER4; 00000613
ARRAY MONOER(4) MONOERl - MONOER4; 00000614
ARRAY UNKWER(4) UNKWER1 - UNKWER4; 00000615
*BIOMASS CATEGORIES HIGH ISOP,LOW ISOP,NO ISOP,CONF- 8 CANOPY LAYERS; 00000616
ARRAY BIOMASS1(8) BIOHI1-BIOHI8; 00000617
ARRAY BIOMASS2(8) BIOLI1-BIOLI8; 00000618
ARRAY BIOMASS3(8) BIONI1-BIONI8; 00000619
ARRAY BIOMASS4(8) BIOCF1-BIOCF8; 00000620
* EMISSIONS FOR JANID, CONF - 8 CANOPY LAYERS; 00000621
ARRAY ISAMT1(8) ISDE1-ISDE8; 00000622
ARRAY MOAMT1(8) MODE1-MODES; 00000623
ARRAY ALAMT1(8) ALDE1-ALDE8; 00000624
ARRAY UNAMT1(8) UNDE1-UNDE8; 00000625
ARRAY ISAMT2(8) ISCF1-ISCF8; 00000626
ARRAY MOAMT2(8) MOCF1-MOCF8; 00000627
ARRAY ALAMT2(8) ALCF1-ALCF8; 00000628
ARRAY UNAMT2(8) UNCF1-UNCF8; 00000629
00000630
* CALC EMISSION RATE; 00000631
DO J - 1 TO 8; 00000632
ISAMTl(J) = (BIOMASSl(J) * ISOPER(l) + 00000633
BIOMASS2(J) * ISOPER(2) + 00000634
BIOMASS3(J) * ISOPER(3)) * CONVR; 00000635
MOAMTl(J) = (BIOMASSl(J) * MONOER(l) + 00000636
BIOMASS2(J) * MONOER(2) + 00000637
BIOMASS3(J) * MONOER(3)) * CONVR; 00000638
ALAMTl(J) - (BIOMASSl(J) * ALPHER(l) -f 00000639
BIOMASS2(J) * ALPHER(2) + 00000640
BIOMASS3(J) * ALPHER(3)) * CONVR; 00000641
UNAMTl(J) -= (BIOMASSl(J) * UNKWER(l) + 00000642
D-30
-------
BIOMASS2(J) * UNKWER(2) +
BIOMASS3(J) * UNKWER(3)) * CONVR;
ISAMT2(J) = BIOMASS4(J) * ISOPER(4) * CONVR;
MOAMT2(J) - BIOMASS4(J) * MONOER(4) * CONVR;
ALAMT2(J) - BIOMASS4(J) * ALPHER(4) * CONVR;
UNAMT2(J) - BIOMASS4(J) * UNKWER(4) * CONVR;
END;
KEEP COL ROW ISDE1-ISDF~ ISCF1-ISCF8 MODE1-MODES MOCF1-MOCF8
ALDE1-ALDE8 ALCF1-ALCF8 UNDE1-UNDE8 UNCF1-UNCF8;
DATA INTER2.GCNPEM; *ADJUST BY HRLY CORRECTION FACTOR TO GET HRLY RATE;
MERGE CNPEMIS IN5.CNPEMFAC;
BY COL ROW;
LENGTH DEFAULT=4;
* HOURLY EMISSION CORRECTION FACTORS - FOR BIOMASS TYPE - 8 LAYERS;
ARRAY MON01(8) MONODE1-MONODE8;
ARRAY ALPH1(8) ALPHDE1-ALPHDE8;
ARRAY ISOP1(8) ISOPDE1-ISOPDE8;
ARRAY MON02(8) MONOCF1-MONOCF8;
ARRAY ALPH2(8) ALPHCF1-ALPHCF8;
ARRAY ISOP2(8) ISOPCF1-ISOPCF8;
* EMISSIONS HIGH ISOP, LOW ISOP, NO ISOP, CONF - 8 CANOPY LAYERS;
ARRAY ISAMT1(8) ISDE1-ISDE8;
ARRAY MOAMT1(8) MODE1-MODE8;
ARRAY ALAMT1(8) ALDE1-ALDE8;
ARRAY UNAMT1(8) UNDE1-UNDE8;
ARRAY ISAMT2(8) ISCF1-ISCF8;
ARRAY MOAMT2(8) MOCF1-MOCF8;
ARRAY ALAMT2(8) ALCF1-ALCF8;
ARRAY UNAMT2(8) UNCF1-UNCF8;
CPISOP = 0;
CPMONO= 0;
CPALPH = 0;
CPUNKW - 0;
* ADJUST HOURLY;
DO J = 1 TO 8;
CPISOP + SUM(ISOP.1(J) * ISAMTl(J)
CPMONO + SUM(MON01(J) * MOAMTl(J)
CPALPH + SUM(ALPH1(J) * ALAMTl(J)
CPUNKW + SUM(MON01(J) * UNAMTl(J)
ISOP2(J)
MON02(J)
ALPH2(J)
* ISAMT2(J))
* MOAMT2(J))
* ALAMT2(J))
MON02(J) * UNAMT2(J))
END;
KEEP COL ROW HOUR CPISOP CPMONO CPALPH CPUNKW;
* COMBINE & READY BIOGENICS FOR RADM
COMBINE CANOPY AND NON-CANOPY EMISSIONS IN G/HR
MERGE THE CANOPY AND THE NON-CANOPY DATA
DATA TEMP.DATA;
MERGE INTER2.GCNPEM INTER1.GNCEM;
LENGTH DEFAULT=4;
BY COL ROW;
* COMBINE & CONVERT FROM G/HR TO G/SEC;
ISOP - SUM(CPISOP, NCISOP)/3600;
MONO = SUM(CPMONO, NCMONO)/3600;
ALPHA - SUM(CPALPH, NCALPH)/3600;
00000643
00000644
00000645
00000646
00000647
00000648
00000649
00000650
00000651
00000652
00000653
00000654
00000655
00000656
00000657
00000658
00000659
00000660
00000661
00000662
00000663
00000664
00000665
00000666
00000667
00000668
00000669
00000670
00000671
00000672
00000673
00000674
00000675
00000676
00000677
00000678
00000679
00000680
00000681
00000682
00000683
00000684
00000685
00000686
00000687
00000688
00000689
00000690
00000691
00000692
00000693
00000694
00000695
00000696
D-3I
-------
UNKW - SUM(CPUNKW, NCUNKW)/3600; 00000697
* CONVERT NO AND N02 FROM MOLES/SEC TO G/SEC; 00000698
BIONO - NCNO * 30.0061; * MW OF N 14.0067 + MW OF 0 15.9994; 00000699
BION02 - NCN02 * 46.0055; * MW OF N 14.0067 + MW OF 2 0 31.9988; 00000700
KLEVEL - 1; * ALL ON LAYER 1; 00000701
RENAME COL-RAD_COL ROW-RAD_ROW; . 00000702
KEEP COL ROW HOUR KLEVEL ISOP MONO ALPHA UNKW BIONO BION02; 00000703
* SORT FOR MERGE WITH POINTS & AREAS; 00000704
PROC SORT DATA-TEMP.DATA OUT-OUT1.RADMBIO; 00000705
BY HOUR RAD_COL RAD_ROW KLEVEL; 00000706
PROC PRINT DATA-OUT1.RADMBIO(OBS=500); 00000707
TITLE1 'COMBINED CANOPY AND NONCANOPY BIOGENIC EMISSIONS'; 00000708
TITLE2 ' FOR RADM INPUT '; 00000709
PROC PRINT DATA=INTER1.GNCEM(OBS-300); 00000710
TITLE 'ADJUSTED GRIDDED HOURLY NONCANOPY EMISSIONS'; 00000711
PROC PRINT DATA=INTER2.GCNPEM(OBS=300); 00000712
TITLE 'ADJUSTED GRIDDED HOURLY CANOPY EMISSIONS'; 00000713
*; 00000720
D-32
-------
FPA- RTF LIBRARY
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/7-91-006
3. RECIPIENT'S ACCESSION NO.
4.TITLE AND SUBTITLE
Development of Seasonal and Annual Biogenic Emis-
sions Inventories for the U.S. and Canada
5. REPORT DATE
November 1991
6. PERFORMING ORGANIZATION CODE
7,AUTHOR(S)
Lysa G. Modica and John R. McCutcheon
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Alliance Technologies Corporation
Foot of John Street
Lowell, Massachusetts 01852
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-D9-0173, Task 1/113
12. SPONSORING AGENCY NAME AND ADDRESS
EPA, Office of Research and Development
Air and Energy Engineering Research Laboratory
Research Triangle Park, North Carolina 27711
13. TYPE OF REPORT AND PERIOD COVERED
Task final; 9/89 - 5/91
14. SPONSORING AGENCY CODE
EPA/600/13
is.SUPPLEMENTARY NOTES AEERL proiect officer is Christopher D. Geron, Mail Drop 63,
919/541-4639. J
16. ABSTRACT
The report describes the development of a biogenic emissions inventory
for the U. S. and Canada, to assess the role of biogenic emissions in ozone forma-
tion. Emission inventories were developed at hourly and grid (1/4 x 1/6 degree) level
from input data at the same scales. Emissions were calculated as a function of bio-
mass density and meteorological parameters (solar radiation, cloud cover, tempera-
ture, windspeed, and relative humidity). These factors were applied to a forest can-
opy algorithm that simulated processes generating biogenic emissions from foliage.
Resultant emissions were aggregated to monthly, seasonal, and annual levels, and
spatially to counties and states. (NOTE: Historically, ozone control programs based
on reductions of known anthropogenic volatile organic compound (VOC) emissions
have had limited success in obtaining the National Ambient Air Quality Standard. Re-
searchers have, therefore, been actively evaluating VOC emission sources not rou-
tinely considered in ozone control strategies. One potentially large source of reac-
tive VOCs is thought to be emissions from crop and forest foliage.) Approximately
50% of the biogenic hydrocarbon emissions occur in the summer, approximately
equal amounts (20%) in the spring and fall, and much lower amounts in the winter.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Pollution
Ozone
Emission
Bioengine er ing
Inventories
Biomass
Meteorology
Organic Compounds
Volatility
Vegetation
Hydrocarbons
Isoprene
Pollution Control
Stationary Sources
Biogenesis
Volatile Organic Com-
pounds (VOCs)
Monoterpenes
13B 04B
07B 07C
14G 20 M
06B 08F
15 E
08A.06C
18. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (ThisReport)
Unclassified
21. NO. OF PAGES
144
20. SECURITY CLASS (This page)
Unclassified
22. PRICE
orm 2220-1 (9-73)
D-33
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